PPE Object Detection by Kirk McGraw¶
Introduction¶
This project investigates the application of deep learning-based object detection models for automated safety monitoring in occupational settings. The primary goal is to refine a computer vision model capable of identifying non-compliance with Personal Protective Equipment (PPE) requirements, as stipulated by OSHA, through the analysis of visual data from on-site surveillance cameras. Companies often have significant financial incentive to ensure PPE compliance, which this project aims to show the viability of automated compliance tracking. This involves training and evaluating models for their accuracy, speed, and generalizability in detecting various PPE items (or their absence). A secondary, related objective is the development of a fall detection component using similar computer vision techniques to reduce the latency of aid to the injured. To that end, this project will evaluate the viability of COCO dataset pretrained Yolov11 and RT-DETR models for safety equipment object-detection with PPE data finetuning.
Dataset¶
The dataset for this project was aquired from Roboflow, PPE combined Dataset, and consists of 30,765 training images, 8,814 validation images, and 4,423 testing images from a variety of industrial settings, outdoor worksites, and youtube still-frames. The data comes already transformed into 640x640 resolution, which is a required format for YOLO models. Futher preprocessing included CLAHE histogram equalization which performs local contrast adaptation without overamplify image noise in homogeneous pixel regions like a global histogram equalization algorithm could. The data consists of 14 classes with a large degree of imbalance amongst them. Ultralytics has functions to help mitigate this, with the use of class normalization and weight penalizing for overrepresented classes.
The initial Yolov11-X exploratory model 1 was trained without frozen weights on 150,000 augmented training images. The learning rate was likely too high based on the erratic precision and loss curves. In addition, we can see an increase in final validation loss when compared to training loss. This is indicative of strong overfitting, and the model will likely generalize poorly on unseen data. This is a potential example of the phenomenon of catastrophic forgetting in which the pretrained weights of an initial model are damaged by the introduction of a high volume of new training data. Due to this initial run and the time complexity of training the whole model, subsequent models had their backbone layers frozen. All models were pretrained with the COCO dataset to ensure comparability. Finetuning occured on the 14 unfrozen convolutional and fully connected layers. Each model incorporates batch normalization according to the maximum VRAM available, as well as an L2 regularization weight decay of 0.0005. This normalization will penalize larger weights to help reduce overfitting. An additional normalization step is the incorporation of 848 unlabeled background images in the training set.
Model Training Results¶
This following table highlights some key metrics from each model. Params is the number of total matrix weights, measured in millions. Higher parameter models tend to be more accurate, but also require increased computational power and processing time. The box loss is a measure of how well the model locates objects by calculating the difference between the position of the labeled and predicted box. The classification loss (CLS) indicates the error in the model's ability to correctly classify the object within each predicted bounding box. This is done using focal loss, which is a variant of the common loss function, cross entropy. The validation metrics help to measure how well the model is generalizing to unseen data, based on the data it has learned in the training set. Lastly the mAP50 is the average precision of all classes, which is derived from the area under its precision-recall curve (AUC). The 50 means that a detection is considered a true positive if its Intersection over Union (IoU) with a ground-truth box of the same class is greater than 0.50.
| # | Model | Params (M) | Architecture | Data Augmentation | Epochs | Learning Rate | Train Box Loss | Validation Box Loss | Train CLS | Validation CLS | mAP50 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Yolov11-X | 56.9 | Single-shot CNN | Yes | 50 | .001 | 0.140 | 0.521 | 0.231 | 1.182 | 0.735 |
| 2 | Yolov11-L | 25.3 | Single-shot CNN | No | 50 | .0001 | 0.155 | 1.360 | 0.252 | 1.066 | 0.752 |
| 3 | Yolov11-L | 25.3 | Single-shot CNN | Yes | 12 | .0001 | 1.165 | 1.285 | 0.687 | 0.767 | 0.784 |
| 4 | Yolov11-L | 25.3 | Single-shot CNN | Yes | 100 | .00001 | 1.082 | 1.266 | 0.630 | 0.778 | 0.770 |
| 5 | RT-DETR-L | 42 | Transformer encode-decode | No | 50 | .0001 | 0.412 | 0.408 | 0.476 | 0.511 | 0.744 |
| 6 | RT-DETR-L | 42 | Transformer encode-decode | Yes | 50 | .0001 | 0.410 | 0.408 | 0.473 | 0.508 | 0.747 |
The overall model results show similar mAP50 across the board, which is interesting considering how much the model size varies. There are fairly diminishing returns in terms of the largest model sizes improving performance only slightly while requiring significantly longer training times. Yolo-L with frozen backbone typically required 1.7 hours, whereas RT-DETR-L was over double at 3.8 hours. The initial Yol011 extra large model 1 suffered from notable overfitting, seen in the increase in validation loss compared to the training loss. Removal of augmentation and a shift to the large model 2 showed similar issues. Seeing this tendancy to overfit as epochs increased, model 3 attempted to mitigate this with early stoping at 12 epochs, which was shown to be a sweet spot in model 2. This approach seemed to offer mixed results. Although training and validation box/CLS loss were higher than other models, the all class average precision was actually the highest. As a followup, model 4 investigated using a lower learning rate across longer epochs, which didn't offer notable improvements over early stoppage. The RT-DETR transformer model showed some of the best validation metrics overall, with little overfitting. The final loss remained similar wether augmentation was applied or not, which suggests the RT-DETR algorithm can potentially generalize better than the Yolo architecture. In the case where the box loss drops noticably after 40 epochs, this is due to mosiac image augmentation stopping at that epoch. Inference speeds were fastest with YOLO, which is an important consideration in practical application, although the RT-DETR inference frames per second was more than sufficient on modern machines.
Model 5 RT-DETR-L precision per class¶
| Metric | Ladder | Goggles | Person | NO-Goggles | Gloves | Fall-Detected | Hardhat | NO-Gloves | Safety Cone | Safety Vest | NO-Hardhat | NO-Mask | Mask | NO-Safety Vest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Average Precision (AuC) | 0.970 | 0.945 | 0.945 | 0.934 | 0.915 | 0.913 | 0.895 | 0.872 | 0.704 | 0.700 | 0.575 | 0.443 | 0.356 | 0.285 |
The relative per class precision metrics remain fairly consistent regardless of model. There are some interesting trends, namely the classifier tends to struggle with negative assignements like no-hardhat, no-mask, and no-safety vest. Initially this was suspected to be due to class imbalance, which could potentially be improved with augmenting the smaller class. However, with further review of the data it appears this may have been a result of poor and inconsistent labeling on a few classes. Safety vest and mask labels appear to be inconsistently applied, which may account for the difficulty the model has in learning these fairly distinctive features. More evidence that this is not due to class imbalance shows from ladders being the least represent class but most accurate class, likely because of the distinctiveness of the ladder feature maps. Without highly accurate and well labeled boxes, the model will simply struggle to learn. This is a limitation of the dataset, rather than the model architecture, which could be improved with additional labeling of unmarked masks and vests. This issue is seemingly present in the some of the NO labels as well.
Overall the model does a great job of detecting hard hats, gloves, goggles, falls, and people in general.
Conclusion and Further Research¶
This project investigated the application of deep learning-based object detection models, specifically YOLOv11 and RT-DETR with COCO pretrained weighta, for automated safety monitoring in occupational settings. Both models achieved high precision in detecting common PPE articles. In addition, the models showed strong capabilities in distinguishing between upright and prone individuals. This helps to establish the viablity of object-detection as a potential early warning safety tool.
Although well known, its important to reiterate the impact of dataset quality on model performance. While class imbalance was initially considered a factor for difficulties in classifying null, or "NO-" prefixed classes further analysis pointed towards inconsistent and potentially poor labeling as a more significant impediment. This was shown by the model's struggle with inconsistently labeled items like masks and safety vests, despite their distinct visual features. Conversely, classes with distinct feature maps and well labeled ground truth achieved high precision even with fewer training examples. These results underscore that model accuracy is fundamentally limited by the quality of the training data. Despite these data limitations, the models demonstrated strong performance in accurately detecting critical safety elements including hard hats, gloves, goggles, falls, and people.
There are some aspects that may warrant further study. Demonstrating the effect on precision of multi-classifier vs single classifier models may offer further accuracy improvements. Further data augmentation and parameters may be worth exploring to further reduce validation increases in the yolo models. Finally, although the COCO dataset is broadly similar to this project's training data, training an architecture from scratch has possible potential, excessive processing demands not withstanding.
In [ ]:
import torch
from ultralytics import YOLO
import os
import yaml
import time
import cv2
import shutil
import numpy as np
from ultralytics import RTDETR
DATASET_YAML = '/home/usr1/Documents/Workspace/PPEdata/data.yaml'
def check_gpu():
"""Checks if a CUDA GPU is available to ensure no CPU processing is done by accident."""
if torch.cuda.is_available():
print(f"CUDA is available. Using GPU: {torch.cuda.get_device_name(0)}")
return True
else:
print("CUDA not available.")
return False
def check_dataset_config(yaml_path):
#Validates the dataset YAML file is properly formatted.
if not os.path.exists(yaml_path):
raise FileNotFoundError(f"Dataset YAML file not found at: {yaml_path}")
try:
with open(yaml_path, 'r') as f:
data_config = yaml.safe_load(f)
print("Dataset configuration loaded successfully:")
print(data_config)
# Basic validation
if 'train' not in data_config or 'val' not in data_config:
raise ValueError("YAML must contain 'train' and 'val' paths.")
if 'nc' not in data_config or 'names' not in data_config:
raise ValueError("YAML must contain 'nc' (number of classes) and 'names' (list of class names).")
if not isinstance(data_config['names'], list) or len(data_config['names']) != data_config['nc']:
raise ValueError("'names' must be a list with length equal to 'nc'.")
return data_config
except Exception as e:
print(f"Error loading or parsing dataset YAML: {e}")
raise
check_gpu()
check_dataset_config(DATASET_YAML)
CUDA is available. Using GPU: NVIDIA GeForce RTX 5090
Dataset configuration loaded successfully:
{'train': '../train/images', 'val': '../valid/images', 'test': '../test/images', 'nc': 14, 'names': ['Fall-Detected', 'Gloves', 'Goggles', 'Hardhat', 'Ladder', 'Mask', 'NO-Gloves', 'NO-Goggles', 'NO-Hardhat', 'NO-Mask', 'NO-Safety Vest', 'Person', 'Safety Cone', 'Safety Vest'], 'roboflow': {'workspace': 'roboflow-universe-projects', 'project': 'personal-protective-equipment-combined-model', 'version': 4, 'license': 'CC BY 4.0', 'url': 'https://universe.roboflow.com/roboflow-universe-projects/personal-protective-equipment-combined-model/dataset/4'}}
Out[ ]:
{'train': '../train/images',
'val': '../valid/images',
'test': '../test/images',
'nc': 14,
'names': ['Fall-Detected',
'Gloves',
'Goggles',
'Hardhat',
'Ladder',
'Mask',
'NO-Gloves',
'NO-Goggles',
'NO-Hardhat',
'NO-Mask',
'NO-Safety Vest',
'Person',
'Safety Cone',
'Safety Vest'],
'roboflow': {'workspace': 'roboflow-universe-projects',
'project': 'personal-protective-equipment-combined-model',
'version': 4,
'license': 'CC BY 4.0',
'url': 'https://universe.roboflow.com/roboflow-universe-projects/personal-protective-equipment-combined-model/dataset/4'}}
Data Preprocessing¶
The first round of data processing was done with local histogram equalization, CLAHE, utlizing the albumentations library. Afterwards, the pipeline was reused for some initial data augmentation, such as hsv, contrast, and guassian blur. This initial augmentation was done with number of augmentations set to 5, which would prove to be excessive.
In [ ]:
import albumentations
ORIGINAL_DIR = '/home/usr1/Documents/Workspace/PPEdata/train/images/'
AUGMENTED_DIR = '/home/usr1/Documents/Workspace/PPEdata/train_aug/images/'
LABELS = '/home/usr1/Documents/Workspace/PPEdata/train/labels/'
AUG_LABELS = '/home/usr1/Documents/Workspace/PPEdata/train_aug/labels/'
#histogram equalization pipeline
equalization_pipeline = A.Compose([
clahe_transform = A.Compose([
A.CLAHE(clip_limit=2.0, tile_grid_size=(8, 8), p=1.0)])
])
# augmentation pipeline
augmentation_pipeline = A.Compose([
A.HueSaturationValue(
hue_shift_limit=0.015 * 180,
sat_shift_limit=0.2,
val_shift_limit=0.2,
p=0.8
),
A.RandomBrightnessContrast(
brightness_limit=0.0,
contrast_limit=0.15,
p=0.8
),
A.GaussianBlur(sigma_limit=(0.5,3),
p=0.8)
])
def augment_images_and_labels(orig_img_path, orig_label_path, aug_img_path, aug_label_path, num_augmentations, augmentation_pipeline):
"""
Applies augmentations to images and copies corresponding label files.
Args:
orig_img_path (str): Path to the directory containing original images.
orig_label_path (str): Path to the directory containing original label files.
aug_img_path (str): Path to the directory where augmented images will be saved.
aug_label_path (str): Path to the directory where copied labels will be saved.
num_augmentations (int): The number of augmented versions to create per original image.
augmentation_pipeline (A.Compose): The albumentations pipeline to apply.
"""
# Create output directories if they don't exist
os.makedirs(aug_img_path, exist_ok=True)
os.makedirs(aug_label_path, exist_ok=True)
print(f"Ensured output directories exist:\n Images: {aug_img_path}\n Labels: {aug_label_path}")
original_image_filenames = []
image_base_names = []
# Iterate through files
for f in os.listdir(orig_img_path):
if f.lower().endswith(".jpg"):
original_image_filenames.append(f)
image_base_names.append(os.path.splitext(f)[0])
print(f"Found {len(original_image_filenames)} potential original images.")
# Stat trackers
start_time = time.time()
processed_img_count = 0
total_saved_img_count = 0
total_copied_lbl_count = 0
skipped_due_missing_label = 0
for img_idx, (base_name, image_filename) in enumerate(zip(image_base_names, original_image_filenames)):
current_image_path = os.path.join(orig_img_path, image_filename)
original_label_filename = f"{base_name}.txt"
current_label_path = os.path.join(orig_label_path, original_label_filename)
print(f"\nProcessing image {img_idx + 1}/{len(original_image_filenames)}: {image_filename}")
#skip an image if its label is missing
if not os.path.exists(current_label_path):
print(f" Label file not found at '{current_label_path}'. Skipping this image.")
skipped_due_missing_label += 1
continue
print(f" Label found: {original_label_filename}")
try:
image = cv2.imread(current_image_path, cv2.IMREAD_COLOR)
if image is None:
print(f" Warning: Failed to load image '{current_image_path}'. Skipping.")
skipped_due_missing_label += 1
continue
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
except Exception as e:
print(f" Error loading image '{current_image_path}': {e}. Skipping.")
skipped_due_missing_label += 1 # Count as skipped if image loading errors out
continue
# Generate and save augmented versions and copy labels
saved_img_count_for_image = 0
copied_lbl_count_for_image = 0
for aug_idx in range(num_augmentations):
augmented_image_data = None
output_image_path = None
output_label_path = None
output_image_filename = f"{base_name}_aug_{aug_idx + 1}.jpg"
output_label_filename = f"{base_name}_aug_{aug_idx + 1}.txt"
try:
augmented = augmentation_pipeline(image=image_rgb)
augmented_image_data = augmented['image']
augmented_image_bgr = cv2.cvtColor(augmented_image_data, cv2.COLOR_RGB2BGR)
output_image_path = os.path.join(aug_img_path, output_image_filename)
# Use the BGR image for writing
write_success = cv2.imwrite(output_image_path, augmented_image_bgr)
if not write_success:
raise IOError(f"cv2.imwrite failed to save to {output_image_path}")
saved_img_count_for_image += 1
total_saved_img_count += 1
# Copy and Rename the Label
output_label_path = os.path.join(aug_label_path, output_label_filename)
shutil.copy2(current_label_path, output_label_path) # copy2 preserves metadata
copied_lbl_count_for_image += 1
total_copied_lbl_count += 1
except Exception as e:
if saved_img_count_for_image > copied_lbl_count_for_image:
print(f" Error copying label for augmented image {output_image_filename} (original label: {current_label_path}): {e}")
if output_image_path and os.path.exists(output_image_path):
print(f" Deleting orphaned augmented image: {output_image_path}")
os.remove(output_image_path)
total_saved_img_count -= 1
saved_img_count_for_image -=1
else:
# Stop trying further augmentations for an image if an error occurs
error_location = f"saving augmented image {output_image_filename}" if augmented_image_data is not None else f"augmenting image {image_filename}"
print(f" Error {error_location}: {e}")
# Log results
if saved_img_count_for_image > 0:
print(f" Successfully saved {saved_img_count_for_image} augmented images and copied {copied_lbl_count_for_image} labels.")
processed_img_count += 1 #
# Summary
end_time = time.time()
print(f"\nAugmentation Complete")
print(f"Processed {processed_img_count}/{len(original_image_filenames)} original images.")
print(f"Skipped {skipped_due_missing_label} images due to missing labels or loading errors.")
print(f"Saved a total of {total_saved_img_count} augmented images to {aug_img_path}")
print(f"Copied a total of {total_copied_lbl_count} corresponding labels to {aug_label_path}")
print(f"Total time taken: {end_time - start_time:.2f} seconds")
if __name__ == "__main__":
# Set the number of augmentations desired per image
NUMBER_OF_AUGMENTATIONS = 5
# Run the augmentation process
augment_images_and_labels(
ORIGINAL_DIR,
LABELS,
AUGMENTED_DIR,
AUG_LABELS,
NUMBER_OF_AUGMENTATIONS,
transform_pipeline
)
Model 1: Yolo11-x¶
In [ ]:
PRETRAINED_YOLOL = '/home/usr1/Documents/Workspace/train_aug/yolo11x.pt'
def train_yolo_model(yaml):
"""
Loads the YOLO model, sets up training parameters and starts the training process.
"""
print("\n--- Starting YOLOv11 Training Setup ---")
device = 0
print(f"\nLoading model: {PRETRAINED_YOLOL}")
try:
model = YOLO(PRETRAINED_YOLOL)
except Exception as e:
print(f"Error loading model '{PRETRAINED_YOLOL}': {e}")
return
print("\nStarting Training")
try:
results = model.train(data=yaml,
epochs=50,
batch=64,
imgsz=IMAGE_SIZE,
device=device,
hsv_h=0.0,
hsv_s=0.0,
hsv_v=0.0,
translate=0.0,
scale=0.0,
fliplr=0.0,
mosaic=0.0,
erasing=0.0,
auto_augment=None,
optimizer='AdamW',
augment=False,
freeze = 10,
lr0=0.0001,
plots=True)
print("\nTraining Finished")
print("Training results:", results)
except Exception as e:
print(f"An error occurred during training: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
# Record start time
start_time = time.time()
print(f"Script started at: {start_time}")
train_yolo_model('/home/usr1/Documents/Workspace/train_aug/data_aug.yaml')
# Record end time and duration
end_time = time.time()
duration = end_time - start_time
print(f"\nScript finished at: {end_time}")
print(f"Total duration: {duration/60}")
Script started at: 1747139222.5196054 --- Starting YOLOv11 Training Setup --- Loading model: /home/usr1/Documents/Workspace/train_aug/yolo11x.pt --- Starting Training --- New https://pypi.org/project/ultralytics/8.3.133 available 😃 Update with 'pip install -U ultralytics' Ultralytics 8.3.127 🚀 Python-3.12.10 torch-2.8.0.dev20250505+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32117MiB) engine/trainer: agnostic_nms=False, amp=True, augment=False, auto_augment=None, batch=64, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/home/usr1/Documents/Workspace/train_aug/data_aug.yaml, degrees=0.0, deterministic=True, device=cuda:0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=50, erasing=0.0, exist_ok=False, fliplr=0.0, flipud=0.0, format=torchscript, fraction=1.0, freeze=16, half=False, hsv_h=0.0, hsv_s=0.0, hsv_v=0.0, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.0001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=/home/usr1/Documents/Workspace/train_aug/yolo11x.pt, momentum=0.937, mosaic=0.0, multi_scale=False, name=train15, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/detect/train15, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.0, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.0, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None Overriding model.yaml nc=80 with nc=14 from n params module arguments 0 -1 1 2784 ultralytics.nn.modules.conv.Conv [3, 96, 3, 2] 1 -1 1 166272 ultralytics.nn.modules.conv.Conv [96, 192, 3, 2] 2 -1 2 389760 ultralytics.nn.modules.block.C3k2 [192, 384, 2, True, 0.25] 3 -1 1 1327872 ultralytics.nn.modules.conv.Conv [384, 384, 3, 2] 4 -1 2 1553664 ultralytics.nn.modules.block.C3k2 [384, 768, 2, True, 0.25] 5 -1 1 5309952 ultralytics.nn.modules.conv.Conv [768, 768, 3, 2] 6 -1 2 5022720 ultralytics.nn.modules.block.C3k2 [768, 768, 2, True] 7 -1 1 5309952 ultralytics.nn.modules.conv.Conv [768, 768, 3, 2] 8 -1 2 5022720 ultralytics.nn.modules.block.C3k2 [768, 768, 2, True] 9 -1 1 1476864 ultralytics.nn.modules.block.SPPF [768, 768, 5] 10 -1 2 3264768 ultralytics.nn.modules.block.C2PSA [768, 768, 2] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 13 -1 2 5612544 ultralytics.nn.modules.block.C3k2 [1536, 768, 2, True] 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 16 -1 2 1700352 ultralytics.nn.modules.block.C3k2 [1536, 384, 2, True] 17 -1 1 1327872 ultralytics.nn.modules.conv.Conv [384, 384, 3, 2] 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] 19 -1 2 5317632 ultralytics.nn.modules.block.C3k2 [1152, 768, 2, True] 20 -1 1 5309952 ultralytics.nn.modules.conv.Conv [768, 768, 3, 2] 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 22 -1 2 5612544 ultralytics.nn.modules.block.C3k2 [1536, 768, 2, True] 23 [16, 19, 22] 1 3161722 ultralytics.nn.modules.head.Detect [14, [384, 768, 768]] YOLO11x summary: 357 layers, 56,889,946 parameters, 56,889,930 gradients, 195.5 GFLOPs Transferred 1009/1015 items from pretrained weights Freezing layer 'model.0.conv.weight' Freezing layer 'model.0.bn.weight' Freezing layer 'model.0.bn.bias' Freezing layer 'model.1.conv.weight' Freezing layer 'model.1.bn.weight' Freezing layer 'model.1.bn.bias' Freezing layer 'model.2.cv1.conv.weight' Freezing layer 'model.2.cv1.bn.weight' Freezing layer 'model.2.cv1.bn.bias' Freezing layer 'model.2.cv2.conv.weight' Freezing layer 'model.2.cv2.bn.weight' Freezing layer 'model.2.cv2.bn.bias' Freezing layer 'model.2.m.0.cv1.conv.weight' Freezing layer 'model.2.m.0.cv1.bn.weight' Freezing layer 'model.2.m.0.cv1.bn.bias' Freezing layer 'model.2.m.0.cv2.conv.weight' Freezing layer 'model.2.m.0.cv2.bn.weight' Freezing layer 'model.2.m.0.cv2.bn.bias' Freezing layer 'model.2.m.0.cv3.conv.weight' Freezing layer 'model.2.m.0.cv3.bn.weight' Freezing layer 'model.2.m.0.cv3.bn.bias' Freezing layer 'model.2.m.0.m.0.cv1.conv.weight' Freezing layer 'model.2.m.0.m.0.cv1.bn.weight' Freezing layer 'model.2.m.0.m.0.cv1.bn.bias' Freezing layer 'model.2.m.0.m.0.cv2.conv.weight' Freezing layer 'model.2.m.0.m.0.cv2.bn.weight' Freezing layer 'model.2.m.0.m.0.cv2.bn.bias' Freezing layer 'model.2.m.0.m.1.cv1.conv.weight' Freezing layer 'model.2.m.0.m.1.cv1.bn.weight' 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'model.13.m.1.m.1.cv2.bn.weight' Freezing layer 'model.13.m.1.m.1.cv2.bn.bias' Freezing layer 'model.23.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ✅ train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 5187.1±2548.0 MB/s, size: 68.1 KB)
train: Scanning /home/usr1/Documents/Workspace/train_aug/train/labels.cache... 153825 images, 4240 backgrounds, 0 corrupt: 100%|██████████| 153825/153825 [00:00<?, ?it/s]
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 3414.1±2447.6 MB/s, size: 65.0 KB)
val: Scanning /home/usr1/Documents/Workspace/train_aug/valid/labels.cache... 8814 images, 210 backgrounds, 0 corrupt: 100%|██████████| 8814/8814 [00:00<?, ?it/s]
Plotting labels to runs/detect/train15/labels.jpg... optimizer: AdamW(lr=0.0001, momentum=0.937) with parameter groups 167 weight(decay=0.0), 174 weight(decay=0.0005), 173 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to runs/detect/train15 Starting training for 50 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/50 20.9G 1.397 1.23 1.38 119 640: 100%|██████████| 2404/2404 [16:40<00:00, 2.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:43<00:00, 1.60it/s]
all 8814 22077 0.641 0.673 0.656 0.395
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/50 20.2G 1.222 0.8908 1.252 92 640: 100%|██████████| 2404/2404 [16:18<00:00, 2.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.64it/s]
all 8814 22077 0.673 0.732 0.713 0.449
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/50 20.6G 1.069 0.7544 1.16 123 640: 100%|██████████| 2404/2404 [16:36<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.62it/s]
all 8814 22077 0.673 0.766 0.721 0.461
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/50 21G 0.8954 0.6405 1.062 75 640: 100%|██████████| 2404/2404 [16:39<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.668 0.784 0.723 0.472
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/50 20.7G 0.7451 0.5564 0.9838 76 640: 100%|██████████| 2404/2404 [16:36<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.676 0.786 0.723 0.474
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/50 20.3G 0.6389 0.5023 0.9326 96 640: 100%|██████████| 2404/2404 [16:22<00:00, 2.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:41<00:00, 1.65it/s]
all 8814 22077 0.677 0.778 0.719 0.474
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/50 20.8G 0.5652 0.4655 0.8992 86 640: 100%|██████████| 2404/2404 [16:33<00:00, 2.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.678 0.774 0.715 0.473
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/50 20.6G 0.5098 0.4371 0.8757 50 640: 100%|██████████| 2404/2404 [16:33<00:00, 2.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.677 0.775 0.713 0.473
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/50 20.6G 0.467 0.4143 0.859 66 640: 100%|██████████| 2404/2404 [16:35<00:00, 2.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.62it/s]
all 8814 22077 0.677 0.77 0.71 0.473
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/50 20.7G 0.4306 0.3952 0.8456 57 640: 100%|██████████| 2404/2404 [16:39<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:43<00:00, 1.60it/s]
all 8814 22077 0.675 0.769 0.709 0.472
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/50 20.6G 0.4017 0.3785 0.8351 87 640: 100%|██████████| 2404/2404 [16:18<00:00, 2.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.64it/s]
all 8814 22077 0.675 0.766 0.707 0.473
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/50 20.3G 0.3782 0.3655 0.8272 71 640: 100%|██████████| 2404/2404 [16:18<00:00, 2.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.64it/s]
all 8814 22077 0.676 0.755 0.705 0.473
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/50 20.8G 0.3559 0.3534 0.8202 67 640: 100%|██████████| 2404/2404 [16:34<00:00, 2.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.676 0.753 0.703 0.472
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/50 20.7G 0.3374 0.3425 0.8147 68 640: 100%|██████████| 2404/2404 [16:18<00:00, 2.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:41<00:00, 1.65it/s]
all 8814 22077 0.669 0.756 0.699 0.47
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/50 20.2G 0.3209 0.3327 0.8096 94 640: 100%|██████████| 2404/2404 [16:38<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.63it/s]
all 8814 22077 0.676 0.748 0.697 0.469
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/50 21.1G 0.3059 0.3241 0.8053 74 640: 100%|██████████| 2404/2404 [16:37<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.674 0.743 0.693 0.467
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/50 20.6G 0.292 0.3163 0.8017 77 640: 100%|██████████| 2404/2404 [16:39<00:00, 2.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.62it/s]
all 8814 22077 0.671 0.739 0.69 0.466
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/50 20.5G 0.2799 0.3087 0.7984 101 640: 100%|██████████| 2404/2404 [16:40<00:00, 2.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.63it/s]
all 8814 22077 0.669 0.741 0.688 0.465
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/50 20.4G 0.2686 0.302 0.7955 84 640: 100%|██████████| 2404/2404 [16:18<00:00, 2.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:41<00:00, 1.66it/s]
all 8814 22077 0.668 0.738 0.685 0.463
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/50 21.1G 0.2584 0.2955 0.7929 114 640: 100%|██████████| 2404/2404 [16:35<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.63it/s]
all 8814 22077 0.668 0.735 0.684 0.462
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/50 20.2G 0.2479 0.2889 0.7905 63 640: 100%|██████████| 2404/2404 [16:38<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.669 0.733 0.683 0.461
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/50 20.5G 0.2385 0.283 0.7884 70 640: 100%|██████████| 2404/2404 [16:39<00:00, 2.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.669 0.727 0.68 0.459
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/50 20.5G 0.2297 0.2771 0.7863 78 640: 100%|██████████| 2404/2404 [16:39<00:00, 2.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.666 0.728 0.678 0.457
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/50 20.6G 0.2218 0.2718 0.7847 84 640: 100%|██████████| 2404/2404 [16:39<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.664 0.722 0.676 0.456
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/50 20.5G 0.214 0.2664 0.783 62 640: 100%|██████████| 2404/2404 [16:19<00:00, 2.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:41<00:00, 1.65it/s]
all 8814 22077 0.661 0.719 0.674 0.455
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/50 20.2G 0.2063 0.2613 0.7813 63 640: 100%|██████████| 2404/2404 [16:28<00:00, 2.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:41<00:00, 1.65it/s]
all 8814 22077 0.659 0.718 0.673 0.454
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/50 20.5G 0.1997 0.2558 0.7801 65 640: 100%|██████████| 2404/2404 [16:37<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.659 0.714 0.671 0.452
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/50 20.5G 0.1929 0.2507 0.7788 73 640: 100%|██████████| 2404/2404 [16:39<00:00, 2.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.62it/s]
all 8814 22077 0.66 0.713 0.669 0.451
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/50 21.1G 0.1867 0.2455 0.7776 74 640: 100%|██████████| 2404/2404 [16:39<00:00, 2.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.62it/s]
all 8814 22077 0.659 0.71 0.668 0.45
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/50 20.2G 0.1805 0.2404 0.7764 57 640: 100%|██████████| 2404/2404 [16:29<00:00, 2.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.64it/s]
all 8814 22077 0.659 0.709 0.666 0.449
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/50 21.1G 0.1745 0.2351 0.7754 95 640: 100%|██████████| 2404/2404 [16:40<00:00, 2.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.658 0.707 0.665 0.448
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/50 21.1G 0.1687 0.23 0.7744 106 640: 100%|██████████| 2404/2404 [16:18<00:00, 2.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.64it/s]
all 8814 22077 0.659 0.703 0.663 0.446
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/50 20.7G 0.163 0.2242 0.7735 72 640: 100%|██████████| 2404/2404 [16:24<00:00, 2.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.62it/s]
all 8814 22077 0.657 0.7 0.661 0.444
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/50 20.2G 0.1576 0.2187 0.7727 87 640: 100%|██████████| 2404/2404 [16:40<00:00, 2.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.657 0.699 0.66 0.443
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/50 21G 0.1521 0.2133 0.7717 74 640: 100%|██████████| 2404/2404 [16:39<00:00, 2.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.655 0.696 0.658 0.441
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/50 21.1G 0.1471 0.2076 0.771 114 640: 100%|██████████| 2404/2404 [16:39<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.62it/s]
all 8814 22077 0.655 0.692 0.655 0.439
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/50 20.7G 0.1421 0.2024 0.7702 87 640: 100%|██████████| 2404/2404 [16:21<00:00, 2.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:41<00:00, 1.65it/s]
all 8814 22077 0.654 0.688 0.653 0.438
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/50 20.5G 0.1371 0.1968 0.7696 69 640: 100%|██████████| 2404/2404 [16:39<00:00, 2.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.651 0.684 0.651 0.436
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/50 20.7G 0.1325 0.1914 0.7688 94 640: 100%|██████████| 2404/2404 [16:19<00:00, 2.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:41<00:00, 1.66it/s]
all 8814 22077 0.65 0.68 0.648 0.435
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/50 20.5G 0.1277 0.1858 0.7683 121 640: 100%|██████████| 2404/2404 [16:36<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.63it/s]
all 8814 22077 0.65 0.677 0.647 0.434
Closing dataloader mosaic
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/50 20.5G 0.1231 0.1798 0.7677 85 640: 100%|██████████| 2404/2404 [16:40<00:00, 2.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.62it/s]
all 8814 22077 0.65 0.671 0.645 0.432
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/50 20.2G 0.1187 0.1748 0.7672 50 640: 100%|██████████| 2404/2404 [16:36<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.63it/s]
all 8814 22077 0.65 0.669 0.644 0.431
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/50 20.3G 0.1143 0.169 0.7665 89 640: 100%|██████████| 2404/2404 [16:36<00:00, 2.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.63it/s]
all 8814 22077 0.649 0.667 0.642 0.429
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/50 20.5G 0.1102 0.1637 0.766 69 640: 100%|██████████| 2404/2404 [16:20<00:00, 2.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.64it/s]
all 8814 22077 0.65 0.662 0.64 0.429
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/50 20.5G 0.1063 0.1583 0.7657 107 640: 100%|██████████| 2404/2404 [17:08<00:00, 2.34it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.61it/s]
all 8814 22077 0.65 0.659 0.638 0.427
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/50 20.5G 0.1024 0.1532 0.7653 64 640: 100%|██████████| 2404/2404 [16:18<00:00, 2.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:41<00:00, 1.66it/s]
all 8814 22077 0.649 0.657 0.637 0.426
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/50 20.6G 0.09864 0.1474 0.7649 62 640: 100%|██████████| 2404/2404 [16:16<00:00, 2.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:41<00:00, 1.65it/s]
all 8814 22077 0.649 0.653 0.635 0.425
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/50 20.4G 0.09514 0.1423 0.7645 70 640: 100%|██████████| 2404/2404 [15:59<00:00, 2.51it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:40<00:00, 1.70it/s]
all 8814 22077 0.648 0.65 0.633 0.424
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/50 20.8G 0.09188 0.1375 0.7643 102 640: 100%|██████████| 2404/2404 [16:16<00:00, 2.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:41<00:00, 1.67it/s]
all 8814 22077 0.647 0.647 0.631 0.422
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/50 21.1G 0.08886 0.1328 0.764 82 640: 100%|██████████| 2404/2404 [16:14<00:00, 2.47it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.64it/s]
all 8814 22077 0.648 0.645 0.629 0.421
50 epochs completed in 14.369 hours. Optimizer stripped from runs/detect/train15/weights/last.pt, 114.4MB Optimizer stripped from runs/detect/train15/weights/best.pt, 114.4MB Validating runs/detect/train15/weights/best.pt... Ultralytics 8.3.127 🚀 Python-3.12.10 torch-2.8.0.dev20250505+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32117MiB) YOLO11x summary (fused): 190 layers, 56,843,194 parameters, 0 gradients, 194.5 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:42<00:00, 1.64it/s]
all 8814 22077 0.676 0.786 0.724 0.474
Fall-Detected 899 899 0.763 0.801 0.825 0.559
Gloves 395 858 0.811 0.916 0.915 0.454
Goggles 746 827 0.838 0.958 0.951 0.602
Hardhat 3191 8952 0.746 0.843 0.819 0.452
Ladder 193 202 0.968 0.901 0.959 0.844
Mask 292 554 0.455 0.879 0.469 0.388
NO-Gloves 571 1258 0.738 0.834 0.851 0.389
NO-Goggles 679 859 0.8 0.939 0.939 0.576
NO-Hardhat 865 2222 0.517 0.857 0.619 0.411
NO-Mask 327 505 0.538 0.784 0.584 0.428
NO-Safety Vest 189 361 0.138 0.179 0.0937 0.0461
Person 193 277 0.931 0.879 0.912 0.76
Safety Cone 338 3016 0.708 0.619 0.639 0.337
Safety Vest 609 1287 0.513 0.608 0.556 0.393
Speed: 0.0ms preprocess, 3.6ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to runs/detect/train15
--- Training Finished ---
Training results: ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7f0d48f903e0>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
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[ 0.97902, 0.97902, 0.97669, ..., 0, 0, 0],
[ 0.99395, 0.99395, 0.99395, ..., 0, 0, 0],
...,
[ 0.91697, 0.91697, 0.91697, ..., 0, 0, 0],
[ 0.76956, 0.76956, 0.75995, ..., 0, 0, 0],
[ 0.91919, 0.91919, 0.90365, ..., 0, 0, 0]], shape=(14, 1000)), 'Confidence', 'Recall']]
fitness: np.float64(0.4991821747658277)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.55943, 0.45355, 0.60204, 0.45236, 0.84422, 0.38759, 0.38919, 0.57584, 0.41119, 0.42801, 0.046053, 0.76037, 0.33695, 0.39251])
names: {0: 'Fall-Detected', 1: 'Gloves', 2: 'Goggles', 3: 'Hardhat', 4: 'Ladder', 5: 'Mask', 6: 'NO-Gloves', 7: 'NO-Goggles', 8: 'NO-Hardhat', 9: 'NO-Mask', 10: 'NO-Safety Vest', 11: 'Person', 12: 'Safety Cone', 13: 'Safety Vest'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.6760303187948269), 'metrics/recall(B)': np.float64(0.7855432175886696), 'metrics/mAP50(B)': np.float64(0.723699458630396), 'metrics/mAP50-95(B)': np.float64(0.47423580989198677), 'fitness': np.float64(0.4991821747658277)}
save_dir: PosixPath('runs/detect/train15')
speed: {'preprocess': 0.03772606568846909, 'inference': 3.641374612331214, 'loss': 2.8554696253379788e-05, 'postprocess': 0.2837713536426569}
task: 'detect'
Script finished at: 1747191006.4405537
Total duration: 863.0653491377831
Model 1 Yolo11-x Results: Loss and per class precision-recall curve¶
Model 2 Yolo11-L¶
In [ ]:
YOLOM_PRETRAINED = "/home/usr1/Documents/Workspace/yolo11l.pt"
# To run model without augmentation, be sure albumentation is uninstalled as ultrayltics will automatically apply augmentation if albumentation library exists
def train_YOLO_model():
"""
Train yolox11-m model with no augmentations and frozen backbone.
"""
print("\nStarting YOLO-M Training Setup")
device = 0
print(f"\nLoading model: {YOLOM_PRETRAINED}")
try:
model = YOLO(YOLOM_PRETRAINED)
except Exception as e:
print(f"Error loading model '{YOLOM_PRETRAINED}': {e}")
return
print("\nStarting Training")
try:
results = model.train(data=DATASET_YAML,
epochs=50,
batch=64,
imgsz=640,
device=device,
hsv_h=0.0,
hsv_s=0.0,
hsv_v=0.0,
translate=0.0,
scale=0.0,
fliplr=0.0,
mosaic=0.0,
erasing=0.0,
auto_augment=None,
optimizer='AdamW',
augment=False,
freeze = 10,
lr0=0.0001,
plots=True)
print("\n Training Finished")
print("Training results:", results)
except Exception as e:
print(f"An error occurred during training: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
start_time = time.time()
print(f"Script started at: {start_time}")
train_YOLO_model()
end_time = time.time()
duration = end_time - start_time
print(f"Total duration: {duration/60}")
Script started at: 1747356017.6395583 Starting YOLO-M Training Setup Loading model: /home/usr1/Documents/Workspace/yolo11l.pt Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt to '/home/usr1/Documents/Workspace/yolo11l.pt'...
100%|██████████| 49.0M/49.0M [00:00<00:00, 75.1MB/s]
Starting Training New https://pypi.org/project/ultralytics/8.3.135 available 😃 Update with 'pip install -U ultralytics' Ultralytics 8.3.127 🚀 Python-3.12.10 torch-2.8.0.dev20250505+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32117MiB) engine/trainer: agnostic_nms=False, amp=True, augment=False, auto_augment=None, batch=64, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/home/usr1/Documents/Workspace/PPEdata/data.yaml, degrees=0.0, deterministic=True, device=cuda:0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=50, erasing=0.0, exist_ok=False, fliplr=0.0, flipud=0.0, format=torchscript, fraction=1.0, freeze=10, half=False, hsv_h=0.0, hsv_s=0.0, hsv_v=0.0, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.0001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=/home/usr1/Documents/Workspace/yolo11l.pt, momentum=0.937, mosaic=0.0, multi_scale=False, name=train30, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/detect/train30, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.0, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.0, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None Overriding model.yaml nc=80 with nc=14 from n params module arguments 0 -1 1 1856 ultralytics.nn.modules.conv.Conv [3, 64, 3, 2] 1 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 2 -1 2 173824 ultralytics.nn.modules.block.C3k2 [128, 256, 2, True, 0.25] 3 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 4 -1 2 691712 ultralytics.nn.modules.block.C3k2 [256, 512, 2, True, 0.25] 5 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] 6 -1 2 2234368 ultralytics.nn.modules.block.C3k2 [512, 512, 2, True] 7 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] 8 -1 2 2234368 ultralytics.nn.modules.block.C3k2 [512, 512, 2, True] 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] 10 -1 2 1455616 ultralytics.nn.modules.block.C2PSA [512, 512, 2] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 13 -1 2 2496512 ultralytics.nn.modules.block.C3k2 [1024, 512, 2, True] 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 16 -1 2 756736 ultralytics.nn.modules.block.C3k2 [1024, 256, 2, True] 17 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] 19 -1 2 2365440 ultralytics.nn.modules.block.C3k2 [768, 512, 2, True] 20 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 22 -1 2 2496512 ultralytics.nn.modules.block.C3k2 [1024, 512, 2, True] 23 [16, 19, 22] 1 1421818 ultralytics.nn.modules.head.Detect [14, [256, 512, 512]] YOLO11l summary: 357 layers, 25,321,274 parameters, 25,321,258 gradients, 87.3 GFLOPs Transferred 1009/1015 items from pretrained weights Freezing layer 'model.0.conv.weight' Freezing layer 'model.0.bn.weight' Freezing layer 'model.0.bn.bias' Freezing layer 'model.1.conv.weight' Freezing layer 'model.1.bn.weight' Freezing layer 'model.1.bn.bias' Freezing layer 'model.2.cv1.conv.weight' Freezing layer 'model.2.cv1.bn.weight' Freezing layer 'model.2.cv1.bn.bias' Freezing layer 'model.2.cv2.conv.weight' Freezing layer 'model.2.cv2.bn.weight' Freezing layer 'model.2.cv2.bn.bias' Freezing layer 'model.2.m.0.cv1.conv.weight' Freezing layer 'model.2.m.0.cv1.bn.weight' Freezing layer 'model.2.m.0.cv1.bn.bias' Freezing layer 'model.2.m.0.cv2.conv.weight' Freezing layer 'model.2.m.0.cv2.bn.weight' Freezing layer 'model.2.m.0.cv2.bn.bias' Freezing layer 'model.2.m.0.cv3.conv.weight' Freezing layer 'model.2.m.0.cv3.bn.weight' Freezing layer 'model.2.m.0.cv3.bn.bias' Freezing layer 'model.2.m.0.m.0.cv1.conv.weight' Freezing layer 'model.2.m.0.m.0.cv1.bn.weight' Freezing layer 'model.2.m.0.m.0.cv1.bn.bias' Freezing layer 'model.2.m.0.m.0.cv2.conv.weight' Freezing layer 'model.2.m.0.m.0.cv2.bn.weight' Freezing layer 'model.2.m.0.m.0.cv2.bn.bias' Freezing layer 'model.2.m.0.m.1.cv1.conv.weight' Freezing layer 'model.2.m.0.m.1.cv1.bn.weight' Freezing layer 'model.2.m.0.m.1.cv1.bn.bias' Freezing layer 'model.2.m.0.m.1.cv2.conv.weight' Freezing layer 'model.2.m.0.m.1.cv2.bn.weight' Freezing layer 'model.2.m.0.m.1.cv2.bn.bias' Freezing layer 'model.2.m.1.cv1.conv.weight' Freezing layer 'model.2.m.1.cv1.bn.weight' Freezing layer 'model.2.m.1.cv1.bn.bias' Freezing layer 'model.2.m.1.cv2.conv.weight' Freezing layer 'model.2.m.1.cv2.bn.weight' Freezing layer 'model.2.m.1.cv2.bn.bias' Freezing layer 'model.2.m.1.cv3.conv.weight' Freezing layer 'model.2.m.1.cv3.bn.weight' Freezing layer 'model.2.m.1.cv3.bn.bias' Freezing layer 'model.2.m.1.m.0.cv1.conv.weight' Freezing layer 'model.2.m.1.m.0.cv1.bn.weight' Freezing layer 'model.2.m.1.m.0.cv1.bn.bias' Freezing layer 'model.2.m.1.m.0.cv2.conv.weight' Freezing layer 'model.2.m.1.m.0.cv2.bn.weight' Freezing layer 'model.2.m.1.m.0.cv2.bn.bias' Freezing layer 'model.2.m.1.m.1.cv1.conv.weight' Freezing layer 'model.2.m.1.m.1.cv1.bn.weight' Freezing layer 'model.2.m.1.m.1.cv1.bn.bias' Freezing layer 'model.2.m.1.m.1.cv2.conv.weight' Freezing layer 'model.2.m.1.m.1.cv2.bn.weight' Freezing layer 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'model.9.cv2.bn.weight' Freezing layer 'model.9.cv2.bn.bias' Freezing layer 'model.23.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ✅ train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 341.9±134.4 MB/s, size: 50.9 KB)
train: Scanning /home/usr1/Documents/Workspace/PPEdata/train/labels.cache... 30765 images, 848 backgrounds, 0 corrupt: 100%|██████████| 30765/30765 [00:00<?, ?it/s]
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 211.9±55.4 MB/s, size: 45.9 KB)
val: Scanning /home/usr1/Documents/Workspace/PPEdata/valid/labels.cache... 8814 images, 210 backgrounds, 0 corrupt: 100%|██████████| 8814/8814 [00:00<?, ?it/s]
Plotting labels to runs/detect/train30/labels.jpg... optimizer: AdamW(lr=0.0001, momentum=0.937) with parameter groups 167 weight(decay=0.0), 174 weight(decay=0.0005), 173 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to runs/detect/train30 Starting training for 50 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/50 18G 1.523 1.555 1.5 122 640: 100%|██████████| 481/481 [02:06<00:00, 3.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:29<00:00, 2.32it/s]
all 8814 22077 0.582 0.577 0.596 0.341
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/50 18.2G 1.412 1.052 1.412 121 640: 100%|██████████| 481/481 [02:07<00:00, 3.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.78it/s]
all 8814 22077 0.595 0.688 0.66 0.385
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/50 18.2G 1.367 0.9536 1.384 133 640: 100%|██████████| 481/481 [02:02<00:00, 3.93it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.77it/s]
all 8814 22077 0.636 0.698 0.699 0.419
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/50 18.2G 1.309 0.8738 1.349 84 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.79it/s]
all 8814 22077 0.704 0.739 0.72 0.443
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/50 18.2G 1.249 0.8001 1.308 100 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.79it/s]
all 8814 22077 0.664 0.762 0.727 0.455
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/50 18.1G 1.192 0.7481 1.273 76 640: 100%|██████████| 481/481 [02:00<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.80it/s]
all 8814 22077 0.654 0.778 0.73 0.463
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/50 18.2G 1.135 0.7053 1.238 91 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.80it/s]
all 8814 22077 0.662 0.786 0.742 0.475
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/50 18.2G 1.082 0.6675 1.204 105 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.80it/s]
all 8814 22077 0.668 0.794 0.741 0.478
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/50 18.1G 1.031 0.6385 1.174 135 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.80it/s]
all 8814 22077 0.679 0.794 0.743 0.477
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/50 18.1G 0.9757 0.6073 1.141 120 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.81it/s]
all 8814 22077 0.681 0.799 0.743 0.478
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/50 18.1G 0.9239 0.5834 1.111 129 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.81it/s]
all 8814 22077 0.692 0.801 0.752 0.491
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/50 18.3G 0.8764 0.5595 1.084 108 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.81it/s]
all 8814 22077 0.696 0.792 0.747 0.488
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/50 18.2G 0.8289 0.539 1.057 105 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.81it/s]
all 8814 22077 0.69 0.809 0.74 0.48
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/50 18.1G 0.7871 0.5233 1.033 90 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.82it/s]
all 8814 22077 0.694 0.8 0.747 0.491
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/50 18.6G 0.7486 0.5059 1.011 141 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.81it/s]
all 8814 22077 0.691 0.8 0.74 0.484
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/50 18.1G 0.7096 0.492 0.991 126 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.82it/s]
all 8814 22077 0.689 0.807 0.738 0.484
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/50 18.3G 0.6753 0.4785 0.9717 83 640: 100%|██████████| 481/481 [02:00<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.82it/s]
all 8814 22077 0.687 0.798 0.736 0.484
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/50 18.2G 0.6413 0.4631 0.9533 110 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.82it/s]
all 8814 22077 0.7 0.797 0.741 0.485
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/50 18.1G 0.6107 0.4495 0.9375 169 640: 100%|██████████| 481/481 [02:05<00:00, 3.83it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:25<00:00, 2.74it/s]
all 8814 22077 0.701 0.796 0.741 0.489
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/50 18.1G 0.583 0.4394 0.9247 113 640: 100%|██████████| 481/481 [02:03<00:00, 3.90it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:25<00:00, 2.74it/s]
all 8814 22077 0.691 0.796 0.73 0.479
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/50 18.2G 0.5563 0.4264 0.9118 86 640: 100%|██████████| 481/481 [02:05<00:00, 3.82it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:25<00:00, 2.76it/s]
all 8814 22077 0.693 0.787 0.732 0.484
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/50 18.2G 0.5336 0.4186 0.9023 108 640: 100%|██████████| 481/481 [02:06<00:00, 3.81it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:27<00:00, 2.47it/s]
all 8814 22077 0.696 0.783 0.732 0.485
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/50 18.1G 0.5106 0.4087 0.8906 114 640: 100%|██████████| 481/481 [02:14<00:00, 3.57it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.80it/s]
all 8814 22077 0.695 0.788 0.733 0.486
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/50 18.1G 0.4886 0.3991 0.8812 139 640: 100%|██████████| 481/481 [02:02<00:00, 3.92it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.80it/s]
all 8814 22077 0.7 0.777 0.732 0.487
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/50 18.1G 0.4684 0.3913 0.8725 128 640: 100%|██████████| 481/481 [02:02<00:00, 3.92it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.84it/s]
all 8814 22077 0.69 0.791 0.729 0.485
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/50 18.2G 0.4495 0.3828 0.8654 99 640: 100%|██████████| 481/481 [02:01<00:00, 3.95it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.76it/s]
all 8814 22077 0.69 0.792 0.728 0.484
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/50 18.1G 0.4329 0.3753 0.8588 100 640: 100%|██████████| 481/481 [02:00<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.82it/s]
all 8814 22077 0.693 0.778 0.727 0.486
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/50 18.1G 0.4163 0.3702 0.852 82 640: 100%|██████████| 481/481 [02:01<00:00, 3.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.78it/s]
all 8814 22077 0.691 0.78 0.725 0.485
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/50 18.2G 0.3991 0.3637 0.8456 137 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.81it/s]
all 8814 22077 0.691 0.779 0.726 0.485
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/50 18.2G 0.3839 0.3552 0.8403 114 640: 100%|██████████| 481/481 [01:59<00:00, 4.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.77it/s]
all 8814 22077 0.691 0.78 0.725 0.485
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/50 18.1G 0.3715 0.3488 0.8364 133 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.84it/s]
all 8814 22077 0.686 0.784 0.725 0.485
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/50 18.4G 0.3561 0.343 0.8315 110 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.84it/s]
all 8814 22077 0.696 0.768 0.725 0.485
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/50 18.1G 0.3388 0.3361 0.8255 126 640: 100%|██████████| 481/481 [02:01<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.84it/s]
all 8814 22077 0.692 0.772 0.724 0.484
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/50 18.2G 0.3287 0.3324 0.8229 107 640: 100%|██████████| 481/481 [02:00<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.83it/s]
all 8814 22077 0.692 0.77 0.723 0.484
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/50 18.1G 0.3123 0.3239 0.8177 119 640: 100%|██████████| 481/481 [02:00<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.84it/s]
all 8814 22077 0.689 0.774 0.722 0.483
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/50 18.1G 0.3039 0.321 0.816 121 640: 100%|██████████| 481/481 [02:03<00:00, 3.88it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:25<00:00, 2.74it/s]
all 8814 22077 0.687 0.777 0.722 0.484
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/50 18.2G 0.2894 0.3155 0.8117 96 640: 100%|██████████| 481/481 [02:03<00:00, 3.91it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.83it/s]
all 8814 22077 0.688 0.775 0.721 0.483
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/50 18.2G 0.2744 0.3072 0.8073 168 640: 100%|██████████| 481/481 [02:07<00:00, 3.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.85it/s]
all 8814 22077 0.689 0.774 0.721 0.484
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/50 18.4G 0.2623 0.3015 0.8044 109 640: 100%|██████████| 481/481 [02:00<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.85it/s]
all 8814 22077 0.692 0.769 0.72 0.483
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/50 18.1G 0.2573 0.3004 0.8047 98 640: 100%|██████████| 481/481 [02:00<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.85it/s]
all 8814 22077 0.689 0.771 0.72 0.483
Closing dataloader mosaic
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/50 18.1G 0.2429 0.2944 0.8001 93 640: 100%|██████████| 481/481 [02:00<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.85it/s]
all 8814 22077 0.688 0.771 0.72 0.483
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/50 17.5G 0.2333 0.2887 0.7987 124 640: 100%|██████████| 481/481 [02:00<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.85it/s]
all 8814 22077 0.688 0.771 0.72 0.483
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/50 18.1G 0.218 0.2825 0.7943 89 640: 100%|██████████| 481/481 [02:00<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.85it/s]
all 8814 22077 0.689 0.77 0.72 0.483
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/50 18.1G 0.2074 0.2771 0.7926 129 640: 100%|██████████| 481/481 [02:00<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.85it/s]
all 8814 22077 0.688 0.772 0.719 0.482
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/50 18.2G 0.1969 0.2727 0.7901 81 640: 100%|██████████| 481/481 [02:00<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:26<00:00, 2.65it/s]
all 8814 22077 0.688 0.772 0.719 0.482
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/50 18.2G 0.1875 0.2679 0.7884 105 640: 100%|██████████| 481/481 [02:04<00:00, 3.87it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.81it/s]
all 8814 22077 0.688 0.772 0.719 0.482
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/50 18.2G 0.1793 0.2648 0.7876 97 640: 100%|██████████| 481/481 [02:02<00:00, 3.92it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:25<00:00, 2.75it/s]
all 8814 22077 0.691 0.769 0.719 0.482
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/50 18.3G 0.1676 0.2589 0.7849 86 640: 100%|██████████| 481/481 [02:13<00:00, 3.61it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:26<00:00, 2.62it/s]
all 8814 22077 0.691 0.77 0.718 0.482
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/50 17.5G 0.1648 0.2579 0.7861 106 640: 100%|██████████| 481/481 [02:08<00:00, 3.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.80it/s]
all 8814 22077 0.69 0.77 0.718 0.482
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/50 18.1G 0.1545 0.2525 0.7838 121 640: 100%|██████████| 481/481 [02:03<00:00, 3.91it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.80it/s]
all 8814 22077 0.689 0.771 0.718 0.481
50 epochs completed in 2.055 hours. Optimizer stripped from runs/detect/train30/weights/last.pt, 51.2MB Optimizer stripped from runs/detect/train30/weights/best.pt, 51.2MB Validating runs/detect/train30/weights/best.pt... Ultralytics 8.3.127 🚀 Python-3.12.10 torch-2.8.0.dev20250505+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32117MiB) YOLO11l summary (fused): 190 layers, 25,290,106 parameters, 0 gradients, 86.6 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:25<00:00, 2.74it/s]
all 8814 22077 0.692 0.801 0.752 0.491
Fall-Detected 899 899 0.723 0.816 0.822 0.546
Gloves 395 858 0.824 0.917 0.92 0.463
Goggles 746 827 0.745 0.938 0.943 0.594
Hardhat 3191 8952 0.781 0.874 0.867 0.477
Ladder 193 202 0.881 0.931 0.947 0.835
Mask 292 554 0.468 0.925 0.507 0.412
NO-Gloves 571 1258 0.769 0.801 0.843 0.397
NO-Goggles 679 859 0.852 0.786 0.908 0.547
NO-Hardhat 865 2222 0.563 0.902 0.721 0.48
NO-Mask 327 505 0.54 0.814 0.617 0.44
NO-Safety Vest 189 361 0.272 0.259 0.181 0.0851
Person 193 277 0.911 0.913 0.938 0.775
Safety Cone 338 3016 0.764 0.597 0.652 0.355
Safety Vest 609 1287 0.595 0.735 0.66 0.469
Speed: 0.0ms preprocess, 1.8ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to runs/detect/train30
Training Finished
Training results: ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7fc7e77b4410>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
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0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,
0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,
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0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,
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0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,
0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,
0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.96218, 0.96218, 0.94994, ..., 0, 0, 0],
[ 0.97436, 0.97436, 0.96737, ..., 0, 0, 0],
[ 0.99274, 0.99274, 0.99274, ..., 0, 0, 0],
...,
[ 0.95307, 0.95307, 0.94946, ..., 0, 0, 0],
[ 0.77288, 0.77288, 0.76658, ..., 0, 0, 0],
[ 0.93939, 0.93939, 0.92852, ..., 0, 0, 0]], shape=(14, 1000)), 'Confidence', 'Recall']]
fitness: np.float64(0.5170937591898765)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.54556, 0.46294, 0.59359, 0.47722, 0.83468, 0.41197, 0.39692, 0.5472, 0.47996, 0.43981, 0.085115, 0.77465, 0.3552, 0.46937])
names: {0: 'Fall-Detected', 1: 'Gloves', 2: 'Goggles', 3: 'Hardhat', 4: 'Ladder', 5: 'Mask', 6: 'NO-Gloves', 7: 'NO-Goggles', 8: 'NO-Hardhat', 9: 'NO-Mask', 10: 'NO-Safety Vest', 11: 'Person', 12: 'Safety Cone', 13: 'Safety Vest'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.6919447972242165), 'metrics/recall(B)': np.float64(0.8005930125424948), 'metrics/mAP50(B)': np.float64(0.7518210252420231), 'metrics/mAP50-95(B)': np.float64(0.4910129518507491), 'fitness': np.float64(0.5170937591898765)}
save_dir: PosixPath('runs/detect/train30')
speed: {'preprocess': 0.03485054118447923, 'inference': 1.8082210157703316, 'loss': 2.8894713128081538e-05, 'postprocess': 0.265140666326332}
task: 'detect'
Total duration: 123.89343286752701
Model 2 Yolo11-L Results: Loss and per class precision-recall curve¶
Model 3 Yolo11-L with Augmentation and short epochs¶
In [ ]:
YOLOM_PRETRAINED = "/home/usr1/Documents/Workspace/yolo11m.pt"
def train_YOLO_model_AUG():
"""
Train Yolov11-m model with augmentations and frozen backbone.
"""
print("\nStarting YOLO-M Training Setup")
device = 0
print(f"\nLoading model: {YOLOM_PRETRAINED}")
try:
model = YOLO(YOLOM_PRETRAINED)
except Exception as e:
print(f"Error loading model '{YOLOM_PRETRAINED}': {e}")
return
print("\nStarting Training")
try:
results = model.train(data=DATASET_YAML,
epochs=12,
batch=64,
imgsz=640,
device=device,
hsv_h=0.015,
hsv_s=0.6,
hsv_v=0.3,
translate=0.1,
scale=0.5,
fliplr=0.5,
mosaic=1.0,
erasing=0.0,
augment=True,
optimizer='AdamW',
lr0=0.0001,
freeze = 10)
print("\n Training Finished")
print("Training results:", results)
except Exception as e:
print(f"An error occurred during training: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
start_time = time.time()
print(f"Script started at: {start_time}")
train_YOLO_model_AUG()
end_time = time.time()
duration = end_time - start_time
print(f"Total duration: {duration/60}")
Script started at: 1747364803.2028127 Starting YOLO-M Training Setup Loading model: /home/usr1/Documents/Workspace/yolo11m.pt Starting Training New https://pypi.org/project/ultralytics/8.3.135 available 😃 Update with 'pip install -U ultralytics' Ultralytics 8.3.127 🚀 Python-3.12.10 torch-2.8.0.dev20250505+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32117MiB) engine/trainer: agnostic_nms=False, amp=True, augment=True, auto_augment=randaugment, batch=64, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/home/usr1/Documents/Workspace/PPEdata/data.yaml, degrees=0.0, deterministic=True, device=cuda:0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=12, erasing=0.0, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=10, half=False, hsv_h=0.015, hsv_s=0.6, hsv_v=0.3, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.0001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=/home/usr1/Documents/Workspace/yolo11m.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=train31, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/detect/train31, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None Overriding model.yaml nc=80 with nc=14 from n params module arguments 0 -1 1 1856 ultralytics.nn.modules.conv.Conv [3, 64, 3, 2] 1 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 2 -1 1 111872 ultralytics.nn.modules.block.C3k2 [128, 256, 1, True, 0.25] 3 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 4 -1 1 444928 ultralytics.nn.modules.block.C3k2 [256, 512, 1, True, 0.25] 5 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] 6 -1 1 1380352 ultralytics.nn.modules.block.C3k2 [512, 512, 1, True] 7 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] 8 -1 1 1380352 ultralytics.nn.modules.block.C3k2 [512, 512, 1, True] 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] 10 -1 1 990976 ultralytics.nn.modules.block.C2PSA [512, 512, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 13 -1 1 1642496 ultralytics.nn.modules.block.C3k2 [1024, 512, 1, True] 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 16 -1 1 542720 ultralytics.nn.modules.block.C3k2 [1024, 256, 1, True] 17 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] 19 -1 1 1511424 ultralytics.nn.modules.block.C3k2 [768, 512, 1, True] 20 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 22 -1 1 1642496 ultralytics.nn.modules.block.C3k2 [1024, 512, 1, True] 23 [16, 19, 22] 1 1421818 ultralytics.nn.modules.head.Detect [14, [256, 512, 512]] YOLO11m summary: 231 layers, 20,063,802 parameters, 20,063,786 gradients, 68.2 GFLOPs Transferred 643/649 items from pretrained weights Freezing layer 'model.0.conv.weight' Freezing layer 'model.0.bn.weight' Freezing layer 'model.0.bn.bias' Freezing layer 'model.1.conv.weight' Freezing layer 'model.1.bn.weight' Freezing layer 'model.1.bn.bias' Freezing layer 'model.2.cv1.conv.weight' Freezing layer 'model.2.cv1.bn.weight' Freezing layer 'model.2.cv1.bn.bias' Freezing layer 'model.2.cv2.conv.weight' Freezing layer 'model.2.cv2.bn.weight' Freezing layer 'model.2.cv2.bn.bias' Freezing layer 'model.2.m.0.cv1.conv.weight' Freezing layer 'model.2.m.0.cv1.bn.weight' Freezing layer 'model.2.m.0.cv1.bn.bias' Freezing layer 'model.2.m.0.cv2.conv.weight' Freezing layer 'model.2.m.0.cv2.bn.weight' Freezing layer 'model.2.m.0.cv2.bn.bias' Freezing layer 'model.2.m.0.cv3.conv.weight' Freezing layer 'model.2.m.0.cv3.bn.weight' Freezing layer 'model.2.m.0.cv3.bn.bias' Freezing layer 'model.2.m.0.m.0.cv1.conv.weight' Freezing layer 'model.2.m.0.m.0.cv1.bn.weight' Freezing layer 'model.2.m.0.m.0.cv1.bn.bias' Freezing layer 'model.2.m.0.m.0.cv2.conv.weight' Freezing layer 'model.2.m.0.m.0.cv2.bn.weight' Freezing layer 'model.2.m.0.m.0.cv2.bn.bias' Freezing layer 'model.2.m.0.m.1.cv1.conv.weight' Freezing layer 'model.2.m.0.m.1.cv1.bn.weight' Freezing layer 'model.2.m.0.m.1.cv1.bn.bias' Freezing layer 'model.2.m.0.m.1.cv2.conv.weight' Freezing layer 'model.2.m.0.m.1.cv2.bn.weight' Freezing layer 'model.2.m.0.m.1.cv2.bn.bias' Freezing layer 'model.3.conv.weight' Freezing layer 'model.3.bn.weight' Freezing layer 'model.3.bn.bias' Freezing layer 'model.4.cv1.conv.weight' Freezing layer 'model.4.cv1.bn.weight' Freezing layer 'model.4.cv1.bn.bias' Freezing layer 'model.4.cv2.conv.weight' Freezing layer 'model.4.cv2.bn.weight' Freezing layer 'model.4.cv2.bn.bias' Freezing layer 'model.4.m.0.cv1.conv.weight' Freezing layer 'model.4.m.0.cv1.bn.weight' Freezing layer 'model.4.m.0.cv1.bn.bias' Freezing layer 'model.4.m.0.cv2.conv.weight' Freezing layer 'model.4.m.0.cv2.bn.weight' Freezing layer 'model.4.m.0.cv2.bn.bias' Freezing layer 'model.4.m.0.cv3.conv.weight' Freezing layer 'model.4.m.0.cv3.bn.weight' Freezing layer 'model.4.m.0.cv3.bn.bias' Freezing layer 'model.4.m.0.m.0.cv1.conv.weight' Freezing layer 'model.4.m.0.m.0.cv1.bn.weight' Freezing layer 'model.4.m.0.m.0.cv1.bn.bias' Freezing layer 'model.4.m.0.m.0.cv2.conv.weight' Freezing layer 'model.4.m.0.m.0.cv2.bn.weight' Freezing layer 'model.4.m.0.m.0.cv2.bn.bias' Freezing layer 'model.4.m.0.m.1.cv1.conv.weight' Freezing layer 'model.4.m.0.m.1.cv1.bn.weight' Freezing layer 'model.4.m.0.m.1.cv1.bn.bias' Freezing layer 'model.4.m.0.m.1.cv2.conv.weight' Freezing layer 'model.4.m.0.m.1.cv2.bn.weight' Freezing layer 'model.4.m.0.m.1.cv2.bn.bias' Freezing layer 'model.5.conv.weight' Freezing layer 'model.5.bn.weight' Freezing layer 'model.5.bn.bias' Freezing layer 'model.6.cv1.conv.weight' Freezing layer 'model.6.cv1.bn.weight' Freezing layer 'model.6.cv1.bn.bias' Freezing layer 'model.6.cv2.conv.weight' Freezing layer 'model.6.cv2.bn.weight' Freezing layer 'model.6.cv2.bn.bias' Freezing layer 'model.6.m.0.cv1.conv.weight' Freezing layer 'model.6.m.0.cv1.bn.weight' Freezing layer 'model.6.m.0.cv1.bn.bias' Freezing layer 'model.6.m.0.cv2.conv.weight' Freezing layer 'model.6.m.0.cv2.bn.weight' Freezing layer 'model.6.m.0.cv2.bn.bias' Freezing layer 'model.6.m.0.cv3.conv.weight' Freezing layer 'model.6.m.0.cv3.bn.weight' Freezing layer 'model.6.m.0.cv3.bn.bias' Freezing layer 'model.6.m.0.m.0.cv1.conv.weight' Freezing layer 'model.6.m.0.m.0.cv1.bn.weight' Freezing layer 'model.6.m.0.m.0.cv1.bn.bias' Freezing layer 'model.6.m.0.m.0.cv2.conv.weight' Freezing layer 'model.6.m.0.m.0.cv2.bn.weight' Freezing layer 'model.6.m.0.m.0.cv2.bn.bias' Freezing layer 'model.6.m.0.m.1.cv1.conv.weight' Freezing layer 'model.6.m.0.m.1.cv1.bn.weight' Freezing layer 'model.6.m.0.m.1.cv1.bn.bias' Freezing layer 'model.6.m.0.m.1.cv2.conv.weight' Freezing layer 'model.6.m.0.m.1.cv2.bn.weight' Freezing layer 'model.6.m.0.m.1.cv2.bn.bias' Freezing layer 'model.7.conv.weight' Freezing layer 'model.7.bn.weight' Freezing layer 'model.7.bn.bias' Freezing layer 'model.8.cv1.conv.weight' Freezing layer 'model.8.cv1.bn.weight' Freezing layer 'model.8.cv1.bn.bias' Freezing layer 'model.8.cv2.conv.weight' Freezing layer 'model.8.cv2.bn.weight' Freezing layer 'model.8.cv2.bn.bias' Freezing layer 'model.8.m.0.cv1.conv.weight' Freezing layer 'model.8.m.0.cv1.bn.weight' Freezing layer 'model.8.m.0.cv1.bn.bias' Freezing layer 'model.8.m.0.cv2.conv.weight' Freezing layer 'model.8.m.0.cv2.bn.weight' Freezing layer 'model.8.m.0.cv2.bn.bias' Freezing layer 'model.8.m.0.cv3.conv.weight' Freezing layer 'model.8.m.0.cv3.bn.weight' Freezing layer 'model.8.m.0.cv3.bn.bias' Freezing layer 'model.8.m.0.m.0.cv1.conv.weight' Freezing layer 'model.8.m.0.m.0.cv1.bn.weight' Freezing layer 'model.8.m.0.m.0.cv1.bn.bias' Freezing layer 'model.8.m.0.m.0.cv2.conv.weight' Freezing layer 'model.8.m.0.m.0.cv2.bn.weight' Freezing layer 'model.8.m.0.m.0.cv2.bn.bias' Freezing layer 'model.8.m.0.m.1.cv1.conv.weight' Freezing layer 'model.8.m.0.m.1.cv1.bn.weight' Freezing layer 'model.8.m.0.m.1.cv1.bn.bias' Freezing layer 'model.8.m.0.m.1.cv2.conv.weight' Freezing layer 'model.8.m.0.m.1.cv2.bn.weight' Freezing layer 'model.8.m.0.m.1.cv2.bn.bias' Freezing layer 'model.9.cv1.conv.weight' Freezing layer 'model.9.cv1.bn.weight' Freezing layer 'model.9.cv1.bn.bias' Freezing layer 'model.9.cv2.conv.weight' Freezing layer 'model.9.cv2.bn.weight' Freezing layer 'model.9.cv2.bn.bias' Freezing layer 'model.23.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ✅ train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 6097.6±3009.6 MB/s, size: 50.9 KB)
train: Scanning /home/usr1/Documents/Workspace/PPEdata/train/labels.cache... 30765 images, 848 backgrounds, 0 corrupt: 100%|██████████| 30765/30765 [00:00<?, ?it/s]
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 4205.5±2252.0 MB/s, size: 45.9 KB)
val: Scanning /home/usr1/Documents/Workspace/PPEdata/valid/labels.cache... 8814 images, 210 backgrounds, 0 corrupt: 100%|██████████| 8814/8814 [00:00<?, ?it/s]
Plotting labels to runs/detect/train31/labels.jpg... optimizer: AdamW(lr=0.0001, momentum=0.937) with parameter groups 106 weight(decay=0.0), 113 weight(decay=0.0005), 112 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to runs/detect/train31 Starting training for 12 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/12 13.9G 1.551 1.69 1.41 159 640: 100%|██████████| 481/481 [01:40<00:00, 4.79it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.19it/s]
all 8814 22077 0.497 0.554 0.53 0.283
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/12 14.9G 1.429 1.221 1.322 189 640: 100%|██████████| 481/481 [01:40<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.18it/s]
all 8814 22077 0.612 0.623 0.61 0.342
Closing dataloader mosaic
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/12 14.9G 1.41 1.063 1.336 93 640: 100%|██████████| 481/481 [01:37<00:00, 4.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.24it/s]
all 8814 22077 0.649 0.719 0.69 0.413
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/12 14.9G 1.371 0.9665 1.302 92 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.23it/s]
all 8814 22077 0.676 0.758 0.728 0.44
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/12 14.9G 1.33 0.8933 1.274 103 640: 100%|██████████| 481/481 [01:47<00:00, 4.48it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.78it/s]
all 8814 22077 0.691 0.776 0.745 0.461
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/12 14.9G 1.299 0.8483 1.251 99 640: 100%|██████████| 481/481 [01:42<00:00, 4.70it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.68 0.795 0.755 0.475
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/12 14.9G 1.273 0.816 1.235 105 640: 100%|██████████| 481/481 [01:36<00:00, 5.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.25it/s]
all 8814 22077 0.695 0.793 0.759 0.484
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/12 15.2G 1.246 0.7793 1.217 89 640: 100%|██████████| 481/481 [01:36<00:00, 5.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.26it/s]
all 8814 22077 0.712 0.797 0.765 0.491
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/12 14.9G 1.222 0.7554 1.203 106 640: 100%|██████████| 481/481 [01:37<00:00, 4.94it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.711 0.812 0.776 0.501
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/12 14.8G 1.204 0.7318 1.189 125 640: 100%|██████████| 481/481 [01:37<00:00, 4.95it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.707 0.824 0.779 0.507
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/12 14.9G 1.185 0.7049 1.179 110 640: 100%|██████████| 481/481 [01:36<00:00, 4.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.711 0.824 0.776 0.508
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/12 14.9G 1.165 0.6867 1.166 111 640: 100%|██████████| 481/481 [01:36<00:00, 4.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.28it/s]
all 8814 22077 0.72 0.822 0.782 0.515
12 epochs completed in 0.403 hours. Optimizer stripped from runs/detect/train31/weights/last.pt, 40.5MB Optimizer stripped from runs/detect/train31/weights/best.pt, 40.5MB Validating runs/detect/train31/weights/best.pt... Ultralytics 8.3.127 🚀 Python-3.12.10 torch-2.8.0.dev20250505+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32117MiB) YOLO11m summary (fused): 125 layers, 20,040,826 parameters, 0 gradients, 67.7 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:38<00:00, 1.78it/s]
all 8814 22077 0.702 0.829 0.784 0.519
Fall-Detected 899 899 0.775 0.889 0.906 0.639
Gloves 395 858 0.795 0.948 0.942 0.499
Goggles 746 827 0.806 0.97 0.963 0.589
Hardhat 3191 8952 0.784 0.913 0.902 0.532
Ladder 193 202 0.903 0.946 0.962 0.866
Mask 292 554 0.465 0.91 0.519 0.413
NO-Gloves 571 1258 0.773 0.852 0.883 0.424
NO-Goggles 679 859 0.795 0.929 0.949 0.539
NO-Hardhat 865 2222 0.599 0.885 0.755 0.503
NO-Mask 327 505 0.547 0.842 0.632 0.443
NO-Safety Vest 189 361 0.329 0.28 0.237 0.119
Person 193 277 0.919 0.895 0.94 0.798
Safety Cone 338 3016 0.707 0.632 0.674 0.375
Safety Vest 609 1287 0.629 0.719 0.71 0.525
Speed: 0.0ms preprocess, 3.3ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to runs/detect/train31
Training Finished
Training results: ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7f9dd4122ea0>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
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[ 0.83588, 0.83588, 0.83289, ..., 0, 0, 0],
[ 0.97747, 0.97747, 0.9697, ..., 0, 0, 0]], shape=(14, 1000)), 'Confidence', 'Recall']]
fitness: np.float64(0.5454418557113005)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.63946, 0.49917, 0.58921, 0.53229, 0.8661, 0.41266, 0.42369, 0.53936, 0.50312, 0.44279, 0.11943, 0.79817, 0.3751, 0.52477])
names: {0: 'Fall-Detected', 1: 'Gloves', 2: 'Goggles', 3: 'Hardhat', 4: 'Ladder', 5: 'Mask', 6: 'NO-Gloves', 7: 'NO-Goggles', 8: 'NO-Hardhat', 9: 'NO-Mask', 10: 'NO-Safety Vest', 11: 'Person', 12: 'Safety Cone', 13: 'Safety Vest'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.7019041565411636), 'metrics/recall(B)': np.float64(0.8292422822971625), 'metrics/mAP50(B)': np.float64(0.7838623950952367), 'metrics/mAP50-95(B)': np.float64(0.5189506846686409), 'fitness': np.float64(0.5454418557113005)}
save_dir: PosixPath('runs/detect/train31')
speed: {'preprocess': 0.03340514465583337, 'inference': 3.338071917971746, 'loss': 2.4015656878727095e-05, 'postprocess': 0.26900873666847674}
task: 'detect'
Total duration: 24.930854698022205
Model 3 Yolo11-L with Augmentation Results: Loss and per class precision-recall curve¶
Model 4 Yolo11-L with Augmentation, lower learning rate, and 100 epochs¶
In [ ]:
YOLOM_PRETRAINED = "/home/usr1/Documents/Workspace/yolo11m.pt"
def train_YOLO_model_AUG():
"""
Train Yolov11-m model with augmentations and frozen backbone for 100 epochs.
"""
print("\nStarting YOLO-M Training Setup")
device = 0
print(f"\nLoading model: {YOLOM_PRETRAINED}")
try:
model = YOLO(YOLOM_PRETRAINED)
except Exception as e:
print(f"Error loading model '{YOLOM_PRETRAINED}': {e}")
return
print("\nStarting Training")
try:
results = model.train(data=DATASET_YAML,
epochs=100,
batch=64,
imgsz=640,
device=device,
hsv_h=0.015,
hsv_s=0.6,
hsv_v=0.3,
translate=0.1,
scale=0.5,
fliplr=0.5,
mosaic=1.0,
erasing=0.0,
augment=True,
optimizer='AdamW',
lr0=0.00001,
freeze = 10)
print("\n Training Finished")
print("Training results:", results)
except Exception as e:
print(f"An error occurred during training: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
start_time = time.time()
print(f"Script started at: {start_time}")
train_YOLO_model_AUG()
end_time = time.time()
duration = end_time - start_time
print(f"Total duration: {duration/60}")
Script started at: 1747407311.4559793 Starting YOLO-M Training Setup Loading model: /home/usr1/Documents/Workspace/yolo11m.pt Starting Training New https://pypi.org/project/ultralytics/8.3.136 available 😃 Update with 'pip install -U ultralytics' Ultralytics 8.3.127 🚀 Python-3.12.10 torch-2.8.0.dev20250505+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32117MiB) engine/trainer: agnostic_nms=False, amp=True, augment=True, auto_augment=randaugment, batch=64, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/home/usr1/Documents/Workspace/PPEdata/data.yaml, degrees=0.0, deterministic=True, device=cuda:0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=100, erasing=0.0, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=10, half=False, hsv_h=0.015, hsv_s=0.6, hsv_v=0.3, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=1e-05, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=/home/usr1/Documents/Workspace/yolo11m.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=train32, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/detect/train32, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None Overriding model.yaml nc=80 with nc=14 from n params module arguments 0 -1 1 1856 ultralytics.nn.modules.conv.Conv [3, 64, 3, 2] 1 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 2 -1 1 111872 ultralytics.nn.modules.block.C3k2 [128, 256, 1, True, 0.25] 3 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 4 -1 1 444928 ultralytics.nn.modules.block.C3k2 [256, 512, 1, True, 0.25] 5 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] 6 -1 1 1380352 ultralytics.nn.modules.block.C3k2 [512, 512, 1, True] 7 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] 8 -1 1 1380352 ultralytics.nn.modules.block.C3k2 [512, 512, 1, True] 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] 10 -1 1 990976 ultralytics.nn.modules.block.C2PSA [512, 512, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 13 -1 1 1642496 ultralytics.nn.modules.block.C3k2 [1024, 512, 1, True] 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 16 -1 1 542720 ultralytics.nn.modules.block.C3k2 [1024, 256, 1, True] 17 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] 19 -1 1 1511424 ultralytics.nn.modules.block.C3k2 [768, 512, 1, True] 20 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 22 -1 1 1642496 ultralytics.nn.modules.block.C3k2 [1024, 512, 1, True] 23 [16, 19, 22] 1 1421818 ultralytics.nn.modules.head.Detect [14, [256, 512, 512]] YOLO11m summary: 231 layers, 20,063,802 parameters, 20,063,786 gradients, 68.2 GFLOPs Transferred 643/649 items from pretrained weights Freezing layer 'model.0.conv.weight' Freezing layer 'model.0.bn.weight' Freezing layer 'model.0.bn.bias' Freezing layer 'model.1.conv.weight' Freezing layer 'model.1.bn.weight' Freezing layer 'model.1.bn.bias' Freezing layer 'model.2.cv1.conv.weight' Freezing layer 'model.2.cv1.bn.weight' Freezing layer 'model.2.cv1.bn.bias' Freezing layer 'model.2.cv2.conv.weight' Freezing layer 'model.2.cv2.bn.weight' Freezing layer 'model.2.cv2.bn.bias' Freezing layer 'model.2.m.0.cv1.conv.weight' Freezing layer 'model.2.m.0.cv1.bn.weight' Freezing layer 'model.2.m.0.cv1.bn.bias' Freezing layer 'model.2.m.0.cv2.conv.weight' Freezing layer 'model.2.m.0.cv2.bn.weight' Freezing layer 'model.2.m.0.cv2.bn.bias' Freezing layer 'model.2.m.0.cv3.conv.weight' Freezing layer 'model.2.m.0.cv3.bn.weight' Freezing layer 'model.2.m.0.cv3.bn.bias' Freezing layer 'model.2.m.0.m.0.cv1.conv.weight' Freezing layer 'model.2.m.0.m.0.cv1.bn.weight' Freezing layer 'model.2.m.0.m.0.cv1.bn.bias' Freezing layer 'model.2.m.0.m.0.cv2.conv.weight' Freezing layer 'model.2.m.0.m.0.cv2.bn.weight' Freezing layer 'model.2.m.0.m.0.cv2.bn.bias' Freezing layer 'model.2.m.0.m.1.cv1.conv.weight' Freezing layer 'model.2.m.0.m.1.cv1.bn.weight' Freezing layer 'model.2.m.0.m.1.cv1.bn.bias' Freezing layer 'model.2.m.0.m.1.cv2.conv.weight' Freezing layer 'model.2.m.0.m.1.cv2.bn.weight' Freezing layer 'model.2.m.0.m.1.cv2.bn.bias' Freezing layer 'model.3.conv.weight' Freezing layer 'model.3.bn.weight' Freezing layer 'model.3.bn.bias' Freezing layer 'model.4.cv1.conv.weight' Freezing layer 'model.4.cv1.bn.weight' Freezing layer 'model.4.cv1.bn.bias' Freezing layer 'model.4.cv2.conv.weight' Freezing layer 'model.4.cv2.bn.weight' Freezing layer 'model.4.cv2.bn.bias' Freezing layer 'model.4.m.0.cv1.conv.weight' Freezing layer 'model.4.m.0.cv1.bn.weight' Freezing layer 'model.4.m.0.cv1.bn.bias' Freezing layer 'model.4.m.0.cv2.conv.weight' Freezing layer 'model.4.m.0.cv2.bn.weight' Freezing layer 'model.4.m.0.cv2.bn.bias' Freezing layer 'model.4.m.0.cv3.conv.weight' Freezing layer 'model.4.m.0.cv3.bn.weight' Freezing layer 'model.4.m.0.cv3.bn.bias' Freezing layer 'model.4.m.0.m.0.cv1.conv.weight' Freezing layer 'model.4.m.0.m.0.cv1.bn.weight' Freezing layer 'model.4.m.0.m.0.cv1.bn.bias' Freezing layer 'model.4.m.0.m.0.cv2.conv.weight' Freezing layer 'model.4.m.0.m.0.cv2.bn.weight' Freezing layer 'model.4.m.0.m.0.cv2.bn.bias' Freezing layer 'model.4.m.0.m.1.cv1.conv.weight' Freezing layer 'model.4.m.0.m.1.cv1.bn.weight' Freezing layer 'model.4.m.0.m.1.cv1.bn.bias' Freezing layer 'model.4.m.0.m.1.cv2.conv.weight' Freezing layer 'model.4.m.0.m.1.cv2.bn.weight' Freezing layer 'model.4.m.0.m.1.cv2.bn.bias' Freezing layer 'model.5.conv.weight' Freezing layer 'model.5.bn.weight' Freezing layer 'model.5.bn.bias' Freezing layer 'model.6.cv1.conv.weight' Freezing layer 'model.6.cv1.bn.weight' Freezing layer 'model.6.cv1.bn.bias' Freezing layer 'model.6.cv2.conv.weight' Freezing layer 'model.6.cv2.bn.weight' Freezing layer 'model.6.cv2.bn.bias' Freezing layer 'model.6.m.0.cv1.conv.weight' Freezing layer 'model.6.m.0.cv1.bn.weight' Freezing layer 'model.6.m.0.cv1.bn.bias' Freezing layer 'model.6.m.0.cv2.conv.weight' Freezing layer 'model.6.m.0.cv2.bn.weight' Freezing layer 'model.6.m.0.cv2.bn.bias' Freezing layer 'model.6.m.0.cv3.conv.weight' Freezing layer 'model.6.m.0.cv3.bn.weight' Freezing layer 'model.6.m.0.cv3.bn.bias' Freezing layer 'model.6.m.0.m.0.cv1.conv.weight' Freezing layer 'model.6.m.0.m.0.cv1.bn.weight' Freezing layer 'model.6.m.0.m.0.cv1.bn.bias' Freezing layer 'model.6.m.0.m.0.cv2.conv.weight' Freezing layer 'model.6.m.0.m.0.cv2.bn.weight' Freezing layer 'model.6.m.0.m.0.cv2.bn.bias' Freezing layer 'model.6.m.0.m.1.cv1.conv.weight' Freezing layer 'model.6.m.0.m.1.cv1.bn.weight' Freezing layer 'model.6.m.0.m.1.cv1.bn.bias' Freezing layer 'model.6.m.0.m.1.cv2.conv.weight' Freezing layer 'model.6.m.0.m.1.cv2.bn.weight' Freezing layer 'model.6.m.0.m.1.cv2.bn.bias' Freezing layer 'model.7.conv.weight' Freezing layer 'model.7.bn.weight' Freezing layer 'model.7.bn.bias' Freezing layer 'model.8.cv1.conv.weight' Freezing layer 'model.8.cv1.bn.weight' Freezing layer 'model.8.cv1.bn.bias' Freezing layer 'model.8.cv2.conv.weight' Freezing layer 'model.8.cv2.bn.weight' Freezing layer 'model.8.cv2.bn.bias' Freezing layer 'model.8.m.0.cv1.conv.weight' Freezing layer 'model.8.m.0.cv1.bn.weight' Freezing layer 'model.8.m.0.cv1.bn.bias' Freezing layer 'model.8.m.0.cv2.conv.weight' Freezing layer 'model.8.m.0.cv2.bn.weight' Freezing layer 'model.8.m.0.cv2.bn.bias' Freezing layer 'model.8.m.0.cv3.conv.weight' Freezing layer 'model.8.m.0.cv3.bn.weight' Freezing layer 'model.8.m.0.cv3.bn.bias' Freezing layer 'model.8.m.0.m.0.cv1.conv.weight' Freezing layer 'model.8.m.0.m.0.cv1.bn.weight' Freezing layer 'model.8.m.0.m.0.cv1.bn.bias' Freezing layer 'model.8.m.0.m.0.cv2.conv.weight' Freezing layer 'model.8.m.0.m.0.cv2.bn.weight' Freezing layer 'model.8.m.0.m.0.cv2.bn.bias' Freezing layer 'model.8.m.0.m.1.cv1.conv.weight' Freezing layer 'model.8.m.0.m.1.cv1.bn.weight' Freezing layer 'model.8.m.0.m.1.cv1.bn.bias' Freezing layer 'model.8.m.0.m.1.cv2.conv.weight' Freezing layer 'model.8.m.0.m.1.cv2.bn.weight' Freezing layer 'model.8.m.0.m.1.cv2.bn.bias' Freezing layer 'model.9.cv1.conv.weight' Freezing layer 'model.9.cv1.bn.weight' Freezing layer 'model.9.cv1.bn.bias' Freezing layer 'model.9.cv2.conv.weight' Freezing layer 'model.9.cv2.bn.weight' Freezing layer 'model.9.cv2.bn.bias' Freezing layer 'model.23.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ✅ train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 336.3±121.4 MB/s, size: 50.9 KB)
train: Scanning /home/usr1/Documents/Workspace/PPEdata/train/labels.cache... 30765 images, 848 backgrounds, 0 corrupt: 100%|██████████| 30765/30765 [00:00<?, ?it/s]
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 224.8±66.1 MB/s, size: 45.9 KB)
val: Scanning /home/usr1/Documents/Workspace/PPEdata/valid/labels.cache... 8814 images, 210 backgrounds, 0 corrupt: 100%|██████████| 8814/8814 [00:00<?, ?it/s]
Plotting labels to runs/detect/train32/labels.jpg... optimizer: AdamW(lr=1e-05, momentum=0.937) with parameter groups 106 weight(decay=0.0), 113 weight(decay=0.0005), 112 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to runs/detect/train32 Starting training for 100 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/100 13.9G 1.59 1.817 1.434 159 640: 100%|██████████| 481/481 [01:36<00:00, 4.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.25it/s]
all 8814 22077 0.45 0.433 0.402 0.192
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/100 14.9G 1.482 1.396 1.344 189 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.23it/s]
all 8814 22077 0.542 0.627 0.584 0.336
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/100 14.9G 1.413 1.245 1.299 179 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.25it/s]
all 8814 22077 0.603 0.682 0.656 0.396
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/100 14G 1.371 1.147 1.268 127 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.25it/s]
all 8814 22077 0.652 0.7 0.687 0.415
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/100 14.5G 1.345 1.088 1.247 154 640: 100%|██████████| 481/481 [01:36<00:00, 5.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.26it/s]
all 8814 22077 0.649 0.725 0.705 0.431
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/100 14.3G 1.332 1.06 1.24 192 640: 100%|██████████| 481/481 [01:36<00:00, 5.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.664 0.724 0.707 0.435
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/100 14G 1.311 1.027 1.226 149 640: 100%|██████████| 481/481 [01:36<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.25it/s]
all 8814 22077 0.669 0.744 0.72 0.446
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/100 14.9G 1.307 1.009 1.226 185 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.25it/s]
all 8814 22077 0.66 0.756 0.723 0.453
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/100 14.2G 1.299 0.9877 1.22 179 640: 100%|██████████| 481/481 [01:36<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.28it/s]
all 8814 22077 0.693 0.75 0.742 0.465
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/100 14G 1.282 0.9662 1.208 220 640: 100%|██████████| 481/481 [01:36<00:00, 5.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.26it/s]
all 8814 22077 0.674 0.767 0.738 0.466
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/100 14.1G 1.281 0.9625 1.209 193 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.25it/s]
all 8814 22077 0.69 0.757 0.744 0.469
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/100 14.8G 1.266 0.9437 1.2 171 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.26it/s]
all 8814 22077 0.693 0.764 0.748 0.474
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/100 15.1G 1.273 0.9389 1.197 134 640: 100%|██████████| 481/481 [01:36<00:00, 5.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.28it/s]
all 8814 22077 0.694 0.775 0.745 0.475
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/100 14G 1.258 0.9217 1.191 148 640: 100%|██████████| 481/481 [01:36<00:00, 5.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.695 0.771 0.748 0.478
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/100 14.8G 1.254 0.9118 1.191 164 640: 100%|██████████| 481/481 [01:36<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.26it/s]
all 8814 22077 0.693 0.788 0.755 0.483
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/100 14G 1.25 0.9046 1.186 185 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.691 0.785 0.752 0.482
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/100 14.9G 1.25 0.904 1.187 164 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.691 0.789 0.753 0.483
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/100 14.9G 1.234 0.8977 1.18 192 640: 100%|██████████| 481/481 [01:36<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.25it/s]
all 8814 22077 0.693 0.784 0.755 0.483
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/100 15.1G 1.234 0.8841 1.178 134 640: 100%|██████████| 481/481 [01:36<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.26it/s]
all 8814 22077 0.7 0.793 0.759 0.487
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/100 14G 1.232 0.8824 1.176 222 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.703 0.79 0.759 0.491
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/100 14G 1.228 0.872 1.176 136 640: 100%|██████████| 481/481 [01:36<00:00, 5.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.701 0.793 0.761 0.493
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/100 14.9G 1.227 0.8703 1.171 164 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.698 0.799 0.763 0.496
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/100 14.3G 1.223 0.8675 1.173 122 640: 100%|██████████| 481/481 [01:36<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.703 0.793 0.762 0.496
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/100 15G 1.216 0.8607 1.166 156 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.703 0.8 0.764 0.499
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/100 14G 1.217 0.8569 1.168 188 640: 100%|██████████| 481/481 [01:36<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:20<00:00, 3.30it/s]
all 8814 22077 0.706 0.798 0.765 0.5
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/100 14G 1.206 0.8448 1.163 130 640: 100%|██████████| 481/481 [01:36<00:00, 5.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.28it/s]
all 8814 22077 0.706 0.804 0.766 0.502
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/100 14G 1.207 0.8468 1.161 110 640: 100%|██████████| 481/481 [01:36<00:00, 5.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.29it/s]
all 8814 22077 0.706 0.803 0.766 0.502
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/100 14.8G 1.204 0.8462 1.159 154 640: 100%|██████████| 481/481 [01:36<00:00, 5.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.28it/s]
all 8814 22077 0.708 0.801 0.766 0.501
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/100 14.8G 1.196 0.8318 1.156 171 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.71 0.802 0.767 0.503
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/100 14.9G 1.196 0.8344 1.152 143 640: 100%|██████████| 481/481 [01:36<00:00, 4.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.707 0.809 0.766 0.502
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/100 14.2G 1.193 0.8306 1.15 128 640: 100%|██████████| 481/481 [01:36<00:00, 5.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.28it/s]
all 8814 22077 0.705 0.811 0.767 0.503
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/100 15G 1.191 0.8239 1.152 162 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.706 0.809 0.767 0.503
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/100 14.9G 1.193 0.823 1.15 136 640: 100%|██████████| 481/481 [01:36<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.703 0.813 0.768 0.503
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/100 14.9G 1.191 0.8243 1.15 202 640: 100%|██████████| 481/481 [01:36<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.705 0.812 0.768 0.504
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/100 14.7G 1.185 0.8118 1.144 188 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.704 0.813 0.768 0.505
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/100 14G 1.18 0.8192 1.143 132 640: 100%|██████████| 481/481 [01:36<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.28it/s]
all 8814 22077 0.704 0.814 0.768 0.505
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/100 14.9G 1.185 0.8156 1.144 192 640: 100%|██████████| 481/481 [01:38<00:00, 4.89it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.01it/s]
all 8814 22077 0.704 0.815 0.768 0.505
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/100 14G 1.172 0.8124 1.142 159 640: 100%|██████████| 481/481 [01:43<00:00, 4.65it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.05it/s]
all 8814 22077 0.706 0.813 0.769 0.506
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/100 15.2G 1.177 0.8069 1.143 162 640: 100%|██████████| 481/481 [01:43<00:00, 4.65it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.01it/s]
all 8814 22077 0.707 0.812 0.769 0.506
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/100 14G 1.175 0.8016 1.138 204 640: 100%|██████████| 481/481 [01:39<00:00, 4.85it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.24it/s]
all 8814 22077 0.705 0.814 0.769 0.506
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/100 14.3G 1.174 0.8053 1.138 191 640: 100%|██████████| 481/481 [01:43<00:00, 4.65it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.07it/s]
all 8814 22077 0.708 0.812 0.769 0.506
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/100 15G 1.17 0.7972 1.137 177 640: 100%|██████████| 481/481 [01:42<00:00, 4.67it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.03it/s]
all 8814 22077 0.708 0.812 0.769 0.507
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/100 14.9G 1.171 0.8028 1.135 198 640: 100%|██████████| 481/481 [01:42<00:00, 4.68it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.03it/s]
all 8814 22077 0.708 0.812 0.769 0.507
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/100 14.9G 1.166 0.7965 1.141 200 640: 100%|██████████| 481/481 [01:43<00:00, 4.66it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.03it/s]
all 8814 22077 0.708 0.813 0.769 0.507
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/100 15G 1.162 0.7935 1.131 174 640: 100%|██████████| 481/481 [01:43<00:00, 4.66it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.06it/s]
all 8814 22077 0.71 0.812 0.77 0.507
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/100 14G 1.163 0.7887 1.131 168 640: 100%|██████████| 481/481 [01:40<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.28it/s]
all 8814 22077 0.708 0.815 0.77 0.508
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/100 14.9G 1.162 0.7924 1.13 128 640: 100%|██████████| 481/481 [01:36<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.19it/s]
all 8814 22077 0.71 0.814 0.77 0.508
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/100 14G 1.154 0.7834 1.128 170 640: 100%|██████████| 481/481 [01:40<00:00, 4.79it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:23<00:00, 3.00it/s]
all 8814 22077 0.709 0.814 0.77 0.508
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/100 14.9G 1.156 0.7839 1.13 194 640: 100%|██████████| 481/481 [01:41<00:00, 4.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.05it/s]
all 8814 22077 0.71 0.814 0.77 0.508
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/100 14G 1.153 0.7782 1.127 141 640: 100%|██████████| 481/481 [01:41<00:00, 4.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.16it/s]
all 8814 22077 0.71 0.814 0.77 0.509
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
51/100 14G 1.152 0.7766 1.127 164 640: 100%|██████████| 481/481 [01:36<00:00, 4.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.21it/s]
all 8814 22077 0.71 0.814 0.77 0.508
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
52/100 14.9G 1.147 0.7748 1.126 167 640: 100%|██████████| 481/481 [01:37<00:00, 4.94it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.24it/s]
all 8814 22077 0.71 0.815 0.77 0.509
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
53/100 15G 1.152 0.7806 1.126 150 640: 100%|██████████| 481/481 [01:36<00:00, 4.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.13it/s]
all 8814 22077 0.709 0.815 0.77 0.509
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
54/100 14.9G 1.145 0.7726 1.127 110 640: 100%|██████████| 481/481 [01:38<00:00, 4.91it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.26it/s]
all 8814 22077 0.706 0.82 0.77 0.509
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
55/100 15.1G 1.144 0.7681 1.125 152 640: 100%|██████████| 481/481 [01:36<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.707 0.819 0.77 0.509
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
56/100 14G 1.14 0.7679 1.124 135 640: 100%|██████████| 481/481 [01:41<00:00, 4.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.06it/s]
all 8814 22077 0.708 0.818 0.77 0.509
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
57/100 15.1G 1.14 0.7639 1.121 153 640: 100%|██████████| 481/481 [01:41<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:20<00:00, 3.30it/s]
all 8814 22077 0.708 0.818 0.77 0.509
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
58/100 14.8G 1.148 0.7682 1.121 177 640: 100%|██████████| 481/481 [01:39<00:00, 4.84it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.05it/s]
all 8814 22077 0.707 0.819 0.77 0.51
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
59/100 14G 1.143 0.7666 1.122 156 640: 100%|██████████| 481/481 [01:39<00:00, 4.83it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.19it/s]
all 8814 22077 0.706 0.82 0.77 0.51
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
60/100 14.9G 1.142 0.764 1.123 163 640: 100%|██████████| 481/481 [01:38<00:00, 4.91it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.18it/s]
all 8814 22077 0.708 0.819 0.77 0.51
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
61/100 14.8G 1.133 0.7588 1.118 177 640: 100%|██████████| 481/481 [01:41<00:00, 4.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.18it/s]
all 8814 22077 0.707 0.82 0.77 0.51
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
62/100 14.9G 1.13 0.7587 1.117 193 640: 100%|██████████| 481/481 [01:41<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.707 0.82 0.77 0.51
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
63/100 14G 1.133 0.7579 1.117 144 640: 100%|██████████| 481/481 [01:35<00:00, 5.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:20<00:00, 3.40it/s]
all 8814 22077 0.707 0.82 0.77 0.51
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
64/100 14.2G 1.127 0.7562 1.116 166 640: 100%|██████████| 481/481 [01:38<00:00, 4.89it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:20<00:00, 3.32it/s]
all 8814 22077 0.707 0.819 0.77 0.51
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
65/100 15.1G 1.131 0.7573 1.115 191 640: 100%|██████████| 481/481 [01:36<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.26it/s]
all 8814 22077 0.707 0.821 0.77 0.51
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
66/100 15G 1.126 0.7531 1.113 193 640: 100%|██████████| 481/481 [01:36<00:00, 4.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.24it/s]
all 8814 22077 0.706 0.822 0.77 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
67/100 15G 1.127 0.7498 1.111 147 640: 100%|██████████| 481/481 [01:36<00:00, 4.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.706 0.821 0.77 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
68/100 14.2G 1.129 0.7572 1.112 133 640: 100%|██████████| 481/481 [01:38<00:00, 4.88it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:23<00:00, 2.96it/s]
all 8814 22077 0.706 0.821 0.77 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
69/100 14.9G 1.127 0.7489 1.113 139 640: 100%|██████████| 481/481 [01:41<00:00, 4.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.17it/s]
all 8814 22077 0.704 0.823 0.77 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
70/100 14G 1.122 0.7481 1.111 192 640: 100%|██████████| 481/481 [01:36<00:00, 4.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.705 0.823 0.77 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
71/100 14.8G 1.123 0.7499 1.111 180 640: 100%|██████████| 481/481 [01:36<00:00, 4.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.24it/s]
all 8814 22077 0.704 0.824 0.77 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
72/100 14.1G 1.122 0.7516 1.11 154 640: 100%|██████████| 481/481 [01:37<00:00, 4.94it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.703 0.825 0.77 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
73/100 14.9G 1.119 0.7426 1.11 153 640: 100%|██████████| 481/481 [01:36<00:00, 4.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:20<00:00, 3.29it/s]
all 8814 22077 0.703 0.825 0.769 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
74/100 14G 1.122 0.7469 1.111 161 640: 100%|██████████| 481/481 [01:47<00:00, 4.49it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:25<00:00, 2.75it/s]
all 8814 22077 0.703 0.825 0.769 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
75/100 14.2G 1.119 0.7435 1.11 164 640: 100%|██████████| 481/481 [01:52<00:00, 4.27it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.78it/s]
all 8814 22077 0.703 0.824 0.769 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
76/100 15.1G 1.12 0.7464 1.109 192 640: 100%|██████████| 481/481 [01:52<00:00, 4.28it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:25<00:00, 2.75it/s]
all 8814 22077 0.703 0.825 0.769 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
77/100 14.9G 1.116 0.7438 1.11 155 640: 100%|██████████| 481/481 [01:53<00:00, 4.23it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:25<00:00, 2.70it/s]
all 8814 22077 0.703 0.825 0.769 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
78/100 15G 1.114 0.7395 1.106 151 640: 100%|██████████| 481/481 [01:53<00:00, 4.22it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:25<00:00, 2.67it/s]
all 8814 22077 0.703 0.825 0.769 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
79/100 15.1G 1.116 0.7423 1.107 153 640: 100%|██████████| 481/481 [01:55<00:00, 4.17it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:25<00:00, 2.67it/s]
all 8814 22077 0.702 0.827 0.769 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
80/100 14.3G 1.118 0.7387 1.109 186 640: 100%|██████████| 481/481 [01:51<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.04it/s]
all 8814 22077 0.704 0.826 0.769 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
81/100 14.9G 1.115 0.7377 1.107 126 640: 100%|██████████| 481/481 [01:53<00:00, 4.23it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:25<00:00, 2.76it/s]
all 8814 22077 0.703 0.826 0.769 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
82/100 15G 1.111 0.7327 1.103 155 640: 100%|██████████| 481/481 [01:51<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.76it/s]
all 8814 22077 0.703 0.825 0.769 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
83/100 15G 1.113 0.739 1.106 168 640: 100%|██████████| 481/481 [01:52<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.81it/s]
all 8814 22077 0.704 0.825 0.769 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
84/100 14.2G 1.105 0.7368 1.102 132 640: 100%|██████████| 481/481 [01:46<00:00, 4.50it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.79it/s]
all 8814 22077 0.704 0.823 0.769 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
85/100 14.9G 1.111 0.735 1.105 139 640: 100%|██████████| 481/481 [01:51<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.79it/s]
all 8814 22077 0.704 0.823 0.769 0.511
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
86/100 14G 1.101 0.7363 1.103 204 640: 100%|██████████| 481/481 [01:51<00:00, 4.32it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.78it/s]
all 8814 22077 0.705 0.823 0.768 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
87/100 14.9G 1.106 0.7311 1.1 176 640: 100%|██████████| 481/481 [01:43<00:00, 4.63it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:24<00:00, 2.77it/s]
all 8814 22077 0.703 0.824 0.768 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
88/100 15.1G 1.109 0.7345 1.103 149 640: 100%|██████████| 481/481 [01:39<00:00, 4.85it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.27it/s]
all 8814 22077 0.703 0.825 0.768 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
89/100 15G 1.107 0.7329 1.101 152 640: 100%|██████████| 481/481 [01:37<00:00, 4.95it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.28it/s]
all 8814 22077 0.704 0.824 0.768 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
90/100 14.8G 1.103 0.7337 1.101 158 640: 100%|██████████| 481/481 [01:36<00:00, 5.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.28it/s]
all 8814 22077 0.704 0.823 0.768 0.512
Closing dataloader mosaic
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
91/100 14.9G 1.1 0.6505 1.112 96 640: 100%|██████████| 481/481 [01:36<00:00, 5.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:20<00:00, 3.29it/s]
all 8814 22077 0.703 0.823 0.768 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
92/100 15.3G 1.093 0.6424 1.106 119 640: 100%|██████████| 481/481 [01:37<00:00, 4.93it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:23<00:00, 2.95it/s]
all 8814 22077 0.703 0.823 0.768 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
93/100 14.9G 1.09 0.6391 1.104 92 640: 100%|██████████| 481/481 [01:43<00:00, 4.64it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.05it/s]
all 8814 22077 0.704 0.823 0.768 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
94/100 14.9G 1.09 0.6357 1.103 91 640: 100%|██████████| 481/481 [01:41<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.13it/s]
all 8814 22077 0.706 0.823 0.769 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
95/100 14.9G 1.086 0.6359 1.103 107 640: 100%|██████████| 481/481 [01:40<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.13it/s]
all 8814 22077 0.706 0.822 0.768 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
96/100 14.9G 1.087 0.6352 1.104 83 640: 100%|██████████| 481/481 [01:40<00:00, 4.81it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.13it/s]
all 8814 22077 0.706 0.821 0.768 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
97/100 14.4G 1.088 0.6344 1.105 85 640: 100%|██████████| 481/481 [01:41<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:23<00:00, 2.96it/s]
all 8814 22077 0.705 0.82 0.768 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
98/100 14.9G 1.083 0.6311 1.102 103 640: 100%|██████████| 481/481 [01:42<00:00, 4.70it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.10it/s]
all 8814 22077 0.706 0.82 0.768 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
99/100 14.9G 1.083 0.6328 1.101 95 640: 100%|██████████| 481/481 [01:40<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:21<00:00, 3.14it/s]
all 8814 22077 0.705 0.822 0.768 0.512
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
100/100 15.1G 1.082 0.6303 1.101 100 640: 100%|██████████| 481/481 [01:40<00:00, 4.79it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:22<00:00, 3.11it/s]
all 8814 22077 0.706 0.821 0.768 0.512
100 epochs completed in 3.404 hours. Optimizer stripped from runs/detect/train32/weights/last.pt, 40.5MB Optimizer stripped from runs/detect/train32/weights/best.pt, 40.5MB Validating runs/detect/train32/weights/best.pt... Ultralytics 8.3.127 🚀 Python-3.12.10 torch-2.8.0.dev20250505+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32117MiB) YOLO11m summary (fused): 125 layers, 20,040,826 parameters, 0 gradients, 67.7 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 69/69 [00:40<00:00, 1.70it/s]
all 8814 22077 0.688 0.832 0.77 0.516
Fall-Detected 899 899 0.77 0.918 0.912 0.666
Gloves 395 858 0.807 0.955 0.949 0.507
Goggles 746 827 0.768 0.977 0.955 0.578
Hardhat 3191 8952 0.778 0.915 0.893 0.528
Ladder 193 202 0.946 0.95 0.963 0.884
Mask 292 554 0.442 0.872 0.454 0.358
NO-Gloves 571 1258 0.767 0.872 0.893 0.435
NO-Goggles 679 859 0.784 0.943 0.945 0.552
NO-Hardhat 865 2222 0.594 0.891 0.728 0.493
NO-Mask 327 505 0.539 0.836 0.604 0.438
NO-Safety Vest 189 361 0.233 0.258 0.182 0.0885
Person 193 277 0.928 0.926 0.949 0.81
Safety Cone 338 3016 0.69 0.642 0.676 0.381
Safety Vest 609 1287 0.594 0.695 0.67 0.506
Speed: 0.0ms preprocess, 3.5ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to runs/detect/train32
Training Finished
Training results: ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7ef72419dd00>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
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...,
[ 0.96029, 0.96029, 0.96029, ..., 0, 0, 0],
[ 0.84416, 0.84416, 0.83952, ..., 0, 0, 0],
[ 0.9728, 0.9728, 0.96503, ..., 0, 0, 0]], shape=(14, 1000)), 'Confidence', 'Recall']]
fitness: np.float64(0.5413891561988542)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.66609, 0.50675, 0.5779, 0.52764, 0.88444, 0.35815, 0.43547, 0.55235, 0.49256, 0.43806, 0.088537, 0.80973, 0.3807, 0.50625])
names: {0: 'Fall-Detected', 1: 'Gloves', 2: 'Goggles', 3: 'Hardhat', 4: 'Ladder', 5: 'Mask', 6: 'NO-Gloves', 7: 'NO-Goggles', 8: 'NO-Hardhat', 9: 'NO-Mask', 10: 'NO-Safety Vest', 11: 'Person', 12: 'Safety Cone', 13: 'Safety Vest'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.688462649314746), 'metrics/recall(B)': np.float64(0.8320279124088633), 'metrics/mAP50(B)': np.float64(0.7695007196368426), 'metrics/mAP50-95(B)': np.float64(0.5160434269279666), 'fitness': np.float64(0.5413891561988542)}
save_dir: PosixPath('runs/detect/train32')
speed: {'preprocess': 0.03450189584707063, 'inference': 3.4874376059682417, 'loss': 2.4285682491770694e-05, 'postprocess': 0.27222587667407494}
task: 'detect'
Total duration: 205.08870148658752
Model 4 Yolo11-L low learning rate and long epoch Results: Loss and per class precision-recall curve¶
Model 5 RT-DETR-L Transformer¶
In [ ]:
DETR_PRETRAINED = "/home/usr1/Documents/Workspace/rtdetr-l.pt"
# To run model without augmentation, be sure albumentation is uninstalled
def train_DETR_model():
"""
Train RT-DETR model with no augmentations and frozen backbone.
"""
print("\nStarting DETR Training Setup")
device = 0
print(f"\nLoading model: {DETR_PRETRAINED}")
try:
model = RTDETR(DETR_PRETRAINED)
except Exception as e:
print(f"Error loading model '{DETR_PRETRAINED}': {e}")
return
print("\nStarting Training")
try:
results = model.train(data=DATASET_YAML,
epochs=50,
batch=32,
imgsz=640,
device=device,
augment=False,
optimizer='AdamW',
lr0=0.0001,
freeze = 10)
print("\n Training Finished")
print("Training results:", results)
except Exception as e:
print(f"An error occurred during training: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
start_time = time.time()
print(f"Script started at: {start_time}")
train_DETR_model()
end_time = time.time()
duration = end_time - start_time
print(f"Total duration: {duration/60}")
Script started at: 1747284785.2352848 Starting DETR Training Setup Loading model: /home/usr1/Documents/Workspace/rtdetr-l.pt --- Starting Training --- New https://pypi.org/project/ultralytics/8.3.134 available 😃 Update with 'pip install -U ultralytics' Ultralytics 8.3.127 🚀 Python-3.12.10 torch-2.8.0.dev20250505+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32120MiB) engine/trainer: agnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=32, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/home/usr1/Documents/Workspace/PPEdata/data.yaml, degrees=0.0, deterministic=True, device=cuda:0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=50, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=10, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.0001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=/home/usr1/Documents/Workspace/rtdetr-l.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=train21, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/detect/train21, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None Overriding model.yaml nc=80 with nc=14 WARNING ⚠️ no model scale passed. Assuming scale='l'. from n params module arguments 0 -1 1 25248 ultralytics.nn.modules.block.HGStem [3, 32, 48] 1 -1 6 155072 ultralytics.nn.modules.block.HGBlock [48, 48, 128, 3, 6] 2 -1 1 1408 ultralytics.nn.modules.conv.DWConv [128, 128, 3, 2, 1, False] 3 -1 6 839296 ultralytics.nn.modules.block.HGBlock [128, 96, 512, 3, 6] 4 -1 1 5632 ultralytics.nn.modules.conv.DWConv [512, 512, 3, 2, 1, False] 5 -1 6 1695360 ultralytics.nn.modules.block.HGBlock [512, 192, 1024, 5, 6, True, False] 6 -1 6 2055808 ultralytics.nn.modules.block.HGBlock [1024, 192, 1024, 5, 6, True, True] 7 -1 6 2055808 ultralytics.nn.modules.block.HGBlock [1024, 192, 1024, 5, 6, True, True] 8 -1 1 11264 ultralytics.nn.modules.conv.DWConv [1024, 1024, 3, 2, 1, False] 9 -1 6 6708480 ultralytics.nn.modules.block.HGBlock [1024, 384, 2048, 5, 6, True, False] 10 -1 1 524800 ultralytics.nn.modules.conv.Conv [2048, 256, 1, 1, None, 1, 1, False] 11 -1 1 789760 ultralytics.nn.modules.transformer.AIFI [256, 1024, 8] 12 -1 1 66048 ultralytics.nn.modules.conv.Conv [256, 256, 1, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 14 7 1 262656 ultralytics.nn.modules.conv.Conv [1024, 256, 1, 1, None, 1, 1, False] 15 [-2, -1] 1 0 ultralytics.nn.modules.conv.Concat [1] 16 -1 3 2232320 ultralytics.nn.modules.block.RepC3 [512, 256, 3] 17 -1 1 66048 ultralytics.nn.modules.conv.Conv [256, 256, 1, 1] 18 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 19 3 1 131584 ultralytics.nn.modules.conv.Conv [512, 256, 1, 1, None, 1, 1, False] 20 [-2, -1] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 3 2232320 ultralytics.nn.modules.block.RepC3 [512, 256, 3] 22 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 23 [-1, 17] 1 0 ultralytics.nn.modules.conv.Concat [1] 24 -1 3 2232320 ultralytics.nn.modules.block.RepC3 [512, 256, 3] 25 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 26 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 27 -1 3 2232320 ultralytics.nn.modules.block.RepC3 [512, 256, 3] 28 [21, 24, 27] 1 7330622 ultralytics.nn.modules.head.RTDETRDecoder [14, [256, 256, 256]] rt-detr-l summary: 457 layers, 32,834,846 parameters, 32,834,846 gradients, 108.0 GFLOPs Transferred 926/941 items from pretrained weights Freezing layer 'model.0.stem1.conv.weight' Freezing layer 'model.0.stem1.bn.weight' Freezing layer 'model.0.stem1.bn.bias' Freezing layer 'model.0.stem2a.conv.weight' Freezing layer 'model.0.stem2a.bn.weight' Freezing layer 'model.0.stem2a.bn.bias' Freezing layer 'model.0.stem2b.conv.weight' Freezing layer 'model.0.stem2b.bn.weight' Freezing layer 'model.0.stem2b.bn.bias' Freezing layer 'model.0.stem3.conv.weight' Freezing layer 'model.0.stem3.bn.weight' Freezing layer 'model.0.stem3.bn.bias' Freezing layer 'model.0.stem4.conv.weight' Freezing layer 'model.0.stem4.bn.weight' Freezing layer 'model.0.stem4.bn.bias' Freezing layer 'model.1.m.0.conv.weight' Freezing layer 'model.1.m.0.bn.weight' Freezing layer 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layer 'model.9.m.4.conv2.bn.bias' Freezing layer 'model.9.m.5.conv1.conv.weight' Freezing layer 'model.9.m.5.conv1.bn.weight' Freezing layer 'model.9.m.5.conv1.bn.bias' Freezing layer 'model.9.m.5.conv2.conv.weight' Freezing layer 'model.9.m.5.conv2.bn.weight' Freezing layer 'model.9.m.5.conv2.bn.bias' Freezing layer 'model.9.sc.conv.weight' Freezing layer 'model.9.sc.bn.weight' Freezing layer 'model.9.sc.bn.bias' Freezing layer 'model.9.ec.conv.weight' Freezing layer 'model.9.ec.bn.weight' Freezing layer 'model.9.ec.bn.bias' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ✅ train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 5134.3±2588.3 MB/s, size: 50.9 KB)
train: Scanning /home/usr1/Documents/Workspace/PPEdata/train/labels.cache... 30765 images, 848 backgrounds, 0 corrupt: 100%|██████████| 30765/30765 [00:00<?, ?it/s]
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 2203.8±1104.5 MB/s, size: 45.9 KB)
val: Scanning /home/usr1/Documents/Workspace/PPEdata/valid/labels.cache... 8814 images, 210 backgrounds, 0 corrupt: 100%|██████████| 8814/8814 [00:00<?, ?it/s]
Plotting labels to runs/detect/train21/labels.jpg... optimizer: AdamW(lr=0.0001, momentum=0.937) with parameter groups 143 weight(decay=0.0), 206 weight(decay=0.0005), 226 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to runs/detect/train21 Starting training for 50 epochs... Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
1/50 17.9G 0.8207 1.411 0.2849 70 640: 100%|██████████| 962/962 [03:52<00:00, 4.14it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.27it/s]
all 8814 22077 0.335 0.123 0.0378 0.0141
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
2/50 14.9G 0.6699 1.104 0.2049 48 640: 100%|██████████| 962/962 [03:49<00:00, 4.19it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.20it/s]
all 8814 22077 0.423 0.304 0.204 0.0925
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
3/50 15.4G 0.61 0.7762 0.2121 27 640: 100%|██████████| 962/962 [03:45<00:00, 4.26it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.498 0.563 0.456 0.256
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
4/50 15G 0.5516 0.6764 0.1967 41 640: 100%|██████████| 962/962 [03:43<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.615 0.652 0.611 0.364
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
5/50 14.8G 0.5321 0.6338 0.188 48 640: 100%|██████████| 962/962 [03:43<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.666 0.646 0.625 0.377
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
6/50 15G 0.5249 0.6114 0.184 39 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.667 0.707 0.667 0.41
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
7/50 15G 0.5148 0.6008 0.1784 48 640: 100%|██████████| 962/962 [03:43<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.636 0.691 0.657 0.403
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
8/50 15G 0.5083 0.5942 0.1764 63 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.651 0.708 0.665 0.409
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
9/50 14.3G 0.505 0.5885 0.175 42 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.67 0.737 0.689 0.43
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
10/50 14.9G 0.5014 0.5812 0.1726 60 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.662 0.756 0.696 0.434
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
11/50 15.5G 0.4946 0.5787 0.1708 58 640: 100%|██████████| 962/962 [03:43<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.673 0.718 0.684 0.428
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
12/50 14.8G 0.4934 0.5716 0.1676 43 640: 100%|██████████| 962/962 [03:43<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.668 0.763 0.708 0.445
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
13/50 14.6G 0.4881 0.5696 0.1673 45 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.671 0.742 0.696 0.44
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
14/50 15.5G 0.4858 0.5685 0.1651 51 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.678 0.751 0.707 0.446
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
15/50 14.9G 0.4812 0.5642 0.1647 81 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.22it/s]
all 8814 22077 0.688 0.776 0.718 0.454
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
16/50 13.9G 0.4834 0.5578 0.1634 98 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.687 0.766 0.717 0.455
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
17/50 14.4G 0.4774 0.556 0.1619 43 640: 100%|██████████| 962/962 [03:43<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.687 0.756 0.711 0.45
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
18/50 14.6G 0.4749 0.5539 0.1626 47 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.683 0.724 0.702 0.445
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
19/50 14.6G 0.4745 0.5518 0.1603 46 640: 100%|██████████| 962/962 [03:43<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.69 0.761 0.719 0.458
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
20/50 14.7G 0.4737 0.5499 0.1598 48 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.687 0.752 0.714 0.454
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
21/50 15.6G 0.4721 0.5499 0.1605 38 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.689 0.763 0.719 0.458
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
22/50 15.6G 0.4717 0.547 0.1575 48 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.695 0.775 0.731 0.468
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
23/50 15.5G 0.4678 0.5452 0.1578 50 640: 100%|██████████| 962/962 [03:43<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.703 0.776 0.732 0.468
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
24/50 14.5G 0.4644 0.5437 0.1575 39 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.695 0.765 0.727 0.464
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
25/50 15.8G 0.465 0.544 0.1595 37 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.689 0.765 0.725 0.464
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
26/50 14.4G 0.4652 0.5426 0.1563 72 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.693 0.773 0.729 0.467
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
27/50 15.1G 0.4619 0.5392 0.1547 57 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.703 0.778 0.734 0.47
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
28/50 15G 0.4599 0.5379 0.1554 45 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.699 0.777 0.733 0.469
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
29/50 15.5G 0.4618 0.5377 0.1542 93 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.701 0.768 0.73 0.467
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
30/50 15.4G 0.4575 0.5337 0.1526 34 640: 100%|██████████| 962/962 [03:43<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.25it/s]
all 8814 22077 0.699 0.772 0.732 0.469
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
31/50 15.5G 0.4594 0.5343 0.154 56 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.702 0.767 0.732 0.469
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
32/50 14.7G 0.4562 0.5353 0.1533 46 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.7 0.771 0.732 0.47
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
33/50 14.6G 0.4556 0.5333 0.1525 56 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.702 0.774 0.733 0.47
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
34/50 14.5G 0.4542 0.5328 0.1527 47 640: 100%|██████████| 962/962 [03:43<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.702 0.776 0.734 0.471
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
35/50 14.6G 0.453 0.5301 0.1521 26 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.705 0.775 0.736 0.473
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
36/50 14.9G 0.453 0.5302 0.151 54 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.704 0.781 0.737 0.474
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
37/50 15.5G 0.453 0.5323 0.1525 69 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.705 0.783 0.739 0.475
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
38/50 15.4G 0.4498 0.5319 0.1518 82 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.704 0.783 0.739 0.475
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
39/50 14.5G 0.4479 0.5306 0.1506 42 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.704 0.784 0.74 0.476
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
40/50 15.5G 0.4502 0.5259 0.1493 37 640: 100%|██████████| 962/962 [03:44<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.704 0.785 0.74 0.476
Closing dataloader mosaic
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
41/50 14.8G 0.4202 0.4897 0.1517 31 640: 100%|██████████| 962/962 [03:43<00:00, 4.31it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.705 0.781 0.74 0.476
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
42/50 14.5G 0.4173 0.4852 0.1502 24 640: 100%|██████████| 962/962 [03:42<00:00, 4.32it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.706 0.782 0.741 0.477
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
43/50 15.8G 0.4167 0.4819 0.1488 17 640: 100%|██████████| 962/962 [03:43<00:00, 4.31it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.709 0.78 0.741 0.477
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
44/50 15.6G 0.4166 0.4808 0.149 44 640: 100%|██████████| 962/962 [03:43<00:00, 4.31it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.709 0.782 0.741 0.478
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
45/50 15.2G 0.4145 0.4801 0.1483 28 640: 100%|██████████| 962/962 [03:42<00:00, 4.32it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.71 0.782 0.741 0.478
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
46/50 15.6G 0.4145 0.4801 0.1474 35 640: 100%|██████████| 962/962 [03:42<00:00, 4.32it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.712 0.782 0.743 0.479
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
47/50 15.8G 0.4132 0.4774 0.1474 28 640: 100%|██████████| 962/962 [03:42<00:00, 4.32it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.711 0.783 0.743 0.479
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
48/50 15.8G 0.414 0.4761 0.1476 20 640: 100%|██████████| 962/962 [03:42<00:00, 4.31it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.711 0.784 0.744 0.481
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
49/50 15.6G 0.4134 0.4769 0.1463 18 640: 100%|██████████| 962/962 [03:42<00:00, 4.32it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.23it/s]
all 8814 22077 0.713 0.782 0.744 0.481
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
50/50 15.6G 0.4116 0.4758 0.1463 38 640: 100%|██████████| 962/962 [03:42<00:00, 4.32it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.24it/s]
all 8814 22077 0.716 0.781 0.745 0.482 50 epochs completed in 3.501 hours. Optimizer stripped from runs/detect/train21/weights/last.pt, 66.2MB Optimizer stripped from runs/detect/train21/weights/best.pt, 66.2MB Validating runs/detect/train21/weights/best.pt... Ultralytics 8.3.127 🚀 Python-3.12.10 torch-2.8.0.dev20250505+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32120MiB) rt-detr-l summary: 302 layers, 32,012,510 parameters, 0 gradients, 103.5 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.21it/s]
all 8814 22077 0.714 0.781 0.744 0.481
Fall-Detected 899 899 0.808 0.908 0.9 0.64
Gloves 395 858 0.824 0.927 0.919 0.471
Goggles 746 827 0.82 0.964 0.946 0.564
Hardhat 3191 8952 0.805 0.91 0.893 0.516
Ladder 193 202 0.891 0.936 0.967 0.818
Mask 292 554 0.438 0.673 0.376 0.282
NO-Gloves 571 1258 0.788 0.842 0.875 0.409
NO-Goggles 679 859 0.811 0.916 0.937 0.539
NO-Hardhat 865 2222 0.632 0.622 0.564 0.367
NO-Mask 327 505 0.543 0.6 0.425 0.29
NO-Safety Vest 189 361 0.34 0.374 0.277 0.146
Person 193 277 0.903 0.924 0.946 0.804
Safety Cone 338 3016 0.788 0.651 0.703 0.386
Safety Vest 609 1287 0.603 0.693 0.685 0.503
Speed: 0.0ms preprocess, 1.8ms inference, 0.0ms loss, 0.1ms postprocess per image
Results saved to runs/detect/train21
Training Finished
Training results: ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7f2371d9d0a0>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
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[ 0.83521, 0.83521, 0.83521, ..., 0, 0, 0],
[ 0.95882, 0.95882, 0.95882, ..., 0, 0, 0]], shape=(14, 1000)), 'Confidence', 'Recall']]
fitness: np.float64(0.5072852083983154)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.6395, 0.47098, 0.56421, 0.51563, 0.81783, 0.28157, 0.40888, 0.53881, 0.3673, 0.29021, 0.14591, 0.80424, 0.38594, 0.50322])
names: {0: 'Fall-Detected', 1: 'Gloves', 2: 'Goggles', 3: 'Hardhat', 4: 'Ladder', 5: 'Mask', 6: 'NO-Gloves', 7: 'NO-Goggles', 8: 'NO-Hardhat', 9: 'NO-Mask', 10: 'NO-Safety Vest', 11: 'Person', 12: 'Safety Cone', 13: 'Safety Vest'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.713905958426646), 'metrics/recall(B)': np.float64(0.7813768808786989), 'metrics/mAP50(B)': np.float64(0.7437140324168717), 'metrics/mAP50-95(B)': np.float64(0.4810153390629202), 'fitness': np.float64(0.5072852083983154)}
save_dir: PosixPath('runs/detect/train21')
speed: {'preprocess': 0.04460776956931149, 'inference': 1.8147868235781204, 'loss': 5.4166666781106074e-05, 'postprocess': 0.10395330269655954}
task: 'detect'
Total duration: 210.64359886248906
Model 5 RT-DETR-L Results: Loss and per class precision-recall curve¶
Model 6 RT-DETR-L with Augmentation¶
In [ ]:
DETR_PRETRAINED = "/home/usr1/Documents/Workspace/rtdetr-l.pt"
def train_DETR_model_AUG():
"""
Rerun DETR model with data augmentation enabled.
"""
print("\nStarting DETR Training Setup")
device = 0
print(f"\nLoading model: {DETR_PRETRAINED}")
try:
model = RTDETR(DETR_PRETRAINED)
except Exception as e:
print(f"Error loading model '{DETR_PRETRAINED}': {e}")
return
print("\nStarting Training")
try:
results = model.train(data=DATASET_YAML,
epochs=50,
batch=32,
imgsz=640,
device=device,
hsv_h=0.015,
hsv_s=0.6,
hsv_v=0.3,
translate=0.1,
scale=0.5,
fliplr=0.5,
mosaic=1.0,
erasing=0.0,
augment=True,
optimizer='AdamW',
lr0=0.0001,
freeze = 10)
print("\n Training Finished")
print("Training results:", results)
except Exception as e:
print(f"An error occurred during training: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
start_time = time.time()
print(f"Script started at: {start_time}")
train_DETR_model_AUG()
end_time = time.time()
duration = end_time - start_time
print(f"Total duration: {duration/60}")
Script started at: 1747331753.05834 Starting DETR Training Setup Loading model: /home/usr1/Documents/Workspace/rtdetr-l.pt --- Starting Training --- New https://pypi.org/project/ultralytics/8.3.135 available 😃 Update with 'pip install -U ultralytics' Ultralytics 8.3.127 🚀 Python-3.12.10 torch-2.8.0.dev20250505+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32120MiB) engine/trainer: agnostic_nms=False, amp=True, augment=True, auto_augment=randaugment, batch=32, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/home/usr1/Documents/Workspace/PPEdata/data.yaml, degrees=0.0, deterministic=True, device=cuda:0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=50, erasing=0.0, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=10, half=False, hsv_h=0.015, hsv_s=0.6, hsv_v=0.3, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.0001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=/home/usr1/Documents/Workspace/rtdetr-l.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=train22, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/detect/train22, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None Overriding model.yaml nc=80 with nc=14 WARNING ⚠️ no model scale passed. Assuming scale='l'. from n params module arguments 0 -1 1 25248 ultralytics.nn.modules.block.HGStem [3, 32, 48] 1 -1 6 155072 ultralytics.nn.modules.block.HGBlock [48, 48, 128, 3, 6] 2 -1 1 1408 ultralytics.nn.modules.conv.DWConv [128, 128, 3, 2, 1, False] 3 -1 6 839296 ultralytics.nn.modules.block.HGBlock [128, 96, 512, 3, 6] 4 -1 1 5632 ultralytics.nn.modules.conv.DWConv [512, 512, 3, 2, 1, False] 5 -1 6 1695360 ultralytics.nn.modules.block.HGBlock [512, 192, 1024, 5, 6, True, False] 6 -1 6 2055808 ultralytics.nn.modules.block.HGBlock [1024, 192, 1024, 5, 6, True, True] 7 -1 6 2055808 ultralytics.nn.modules.block.HGBlock [1024, 192, 1024, 5, 6, True, True] 8 -1 1 11264 ultralytics.nn.modules.conv.DWConv [1024, 1024, 3, 2, 1, False] 9 -1 6 6708480 ultralytics.nn.modules.block.HGBlock [1024, 384, 2048, 5, 6, True, False] 10 -1 1 524800 ultralytics.nn.modules.conv.Conv [2048, 256, 1, 1, None, 1, 1, False] 11 -1 1 789760 ultralytics.nn.modules.transformer.AIFI [256, 1024, 8] 12 -1 1 66048 ultralytics.nn.modules.conv.Conv [256, 256, 1, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 14 7 1 262656 ultralytics.nn.modules.conv.Conv [1024, 256, 1, 1, None, 1, 1, False] 15 [-2, -1] 1 0 ultralytics.nn.modules.conv.Concat [1] 16 -1 3 2232320 ultralytics.nn.modules.block.RepC3 [512, 256, 3] 17 -1 1 66048 ultralytics.nn.modules.conv.Conv [256, 256, 1, 1] 18 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 19 3 1 131584 ultralytics.nn.modules.conv.Conv [512, 256, 1, 1, None, 1, 1, False] 20 [-2, -1] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 3 2232320 ultralytics.nn.modules.block.RepC3 [512, 256, 3] 22 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 23 [-1, 17] 1 0 ultralytics.nn.modules.conv.Concat [1] 24 -1 3 2232320 ultralytics.nn.modules.block.RepC3 [512, 256, 3] 25 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 26 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 27 -1 3 2232320 ultralytics.nn.modules.block.RepC3 [512, 256, 3] 28 [21, 24, 27] 1 7330622 ultralytics.nn.modules.head.RTDETRDecoder [14, [256, 256, 256]] rt-detr-l summary: 457 layers, 32,834,846 parameters, 32,834,846 gradients, 108.0 GFLOPs Transferred 926/941 items from pretrained weights Freezing layer 'model.0.stem1.conv.weight' Freezing layer 'model.0.stem1.bn.weight' Freezing layer 'model.0.stem1.bn.bias' Freezing layer 'model.0.stem2a.conv.weight' Freezing layer 'model.0.stem2a.bn.weight' Freezing layer 'model.0.stem2a.bn.bias' Freezing layer 'model.0.stem2b.conv.weight' Freezing layer 'model.0.stem2b.bn.weight' Freezing layer 'model.0.stem2b.bn.bias' Freezing layer 'model.0.stem3.conv.weight' Freezing layer 'model.0.stem3.bn.weight' Freezing layer 'model.0.stem3.bn.bias' Freezing layer 'model.0.stem4.conv.weight' Freezing layer 'model.0.stem4.bn.weight' Freezing layer 'model.0.stem4.bn.bias' Freezing layer 'model.1.m.0.conv.weight' Freezing layer 'model.1.m.0.bn.weight' Freezing layer 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layer 'model.9.m.4.conv2.bn.bias' Freezing layer 'model.9.m.5.conv1.conv.weight' Freezing layer 'model.9.m.5.conv1.bn.weight' Freezing layer 'model.9.m.5.conv1.bn.bias' Freezing layer 'model.9.m.5.conv2.conv.weight' Freezing layer 'model.9.m.5.conv2.bn.weight' Freezing layer 'model.9.m.5.conv2.bn.bias' Freezing layer 'model.9.sc.conv.weight' Freezing layer 'model.9.sc.bn.weight' Freezing layer 'model.9.sc.bn.bias' Freezing layer 'model.9.ec.conv.weight' Freezing layer 'model.9.ec.bn.weight' Freezing layer 'model.9.ec.bn.bias' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ✅ train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 333.7±134.6 MB/s, size: 50.9 KB)
train: Scanning /home/usr1/Documents/Workspace/PPEdata/train/labels.cache... 30765 images, 848 backgrounds, 0 corrupt: 100%|██████████| 30765/30765 [00:00<?, ?it/s]
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 186.9±54.9 MB/s, size: 45.9 KB)
val: Scanning /home/usr1/Documents/Workspace/PPEdata/valid/labels.cache... 8814 images, 210 backgrounds, 0 corrupt: 100%|██████████| 8814/8814 [00:00<?, ?it/s]
Plotting labels to runs/detect/train22/labels.jpg... optimizer: AdamW(lr=0.0001, momentum=0.937) with parameter groups 143 weight(decay=0.0), 206 weight(decay=0.0005), 226 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to runs/detect/train22 Starting training for 50 epochs... Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
1/50 13.9G 0.8142 1.439 0.2908 70 640: 100%|██████████| 962/962 [04:12<00:00, 3.81it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:28<00:00, 4.79it/s]
all 8814 22077 0.672 0.112 0.0609 0.0303
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
2/50 14.2G 0.6221 1.144 0.1961 48 640: 100%|██████████| 962/962 [04:29<00:00, 3.57it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:31<00:00, 4.40it/s]
all 8814 22077 0.317 0.399 0.215 0.095
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
3/50 15G 0.6193 0.7579 0.2136 27 640: 100%|██████████| 962/962 [04:36<00:00, 3.48it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:32<00:00, 4.24it/s]
all 8814 22077 0.514 0.572 0.472 0.265
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
4/50 14G 0.5511 0.6664 0.1973 41 640: 100%|██████████| 962/962 [04:25<00:00, 3.62it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.67it/s]
all 8814 22077 0.625 0.632 0.611 0.361
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
5/50 14G 0.5333 0.6208 0.1896 48 640: 100%|██████████| 962/962 [03:42<00:00, 4.32it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:28<00:00, 4.83it/s]
all 8814 22077 0.669 0.645 0.632 0.382
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
6/50 13.8G 0.524 0.6027 0.1837 39 640: 100%|██████████| 962/962 [04:05<00:00, 3.93it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.74it/s]
all 8814 22077 0.668 0.713 0.675 0.413
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
7/50 14G 0.5147 0.5912 0.1785 48 640: 100%|██████████| 962/962 [04:06<00:00, 3.90it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.73it/s]
all 8814 22077 0.645 0.67 0.657 0.399
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
8/50 13.6G 0.507 0.5831 0.1763 63 640: 100%|██████████| 962/962 [04:06<00:00, 3.90it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.73it/s]
all 8814 22077 0.675 0.701 0.674 0.414
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
9/50 13.8G 0.503 0.5782 0.1745 42 640: 100%|██████████| 962/962 [04:06<00:00, 3.90it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.75it/s]
all 8814 22077 0.686 0.737 0.703 0.435
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
10/50 14.2G 0.5005 0.5728 0.1726 60 640: 100%|██████████| 962/962 [04:07<00:00, 3.89it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.72it/s]
all 8814 22077 0.678 0.702 0.679 0.419
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
11/50 14.9G 0.493 0.5707 0.1699 58 640: 100%|██████████| 962/962 [04:07<00:00, 3.89it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.75it/s]
all 8814 22077 0.679 0.71 0.691 0.428
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
12/50 14G 0.4923 0.5605 0.1675 43 640: 100%|██████████| 962/962 [04:06<00:00, 3.90it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.75it/s]
all 8814 22077 0.692 0.755 0.715 0.448
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
13/50 13.8G 0.487 0.5594 0.1672 45 640: 100%|██████████| 962/962 [04:07<00:00, 3.89it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.75it/s]
all 8814 22077 0.689 0.733 0.701 0.44
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
14/50 15G 0.485 0.5613 0.1644 51 640: 100%|██████████| 962/962 [04:05<00:00, 3.91it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.74it/s]
all 8814 22077 0.689 0.753 0.712 0.449
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
15/50 14.7G 0.4794 0.5567 0.1639 81 640: 100%|██████████| 962/962 [04:07<00:00, 3.89it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.74it/s]
all 8814 22077 0.701 0.764 0.72 0.455
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
16/50 13.3G 0.4804 0.5511 0.1626 98 640: 100%|██████████| 962/962 [04:08<00:00, 3.88it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.63it/s]
all 8814 22077 0.704 0.76 0.721 0.456
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
17/50 14.3G 0.4762 0.5473 0.1612 43 640: 100%|██████████| 962/962 [04:11<00:00, 3.82it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.63it/s]
all 8814 22077 0.705 0.758 0.723 0.457
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
18/50 14.3G 0.4729 0.5481 0.1623 47 640: 100%|██████████| 962/962 [04:12<00:00, 3.81it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.61it/s]
all 8814 22077 0.695 0.74 0.714 0.45
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
19/50 14.2G 0.4733 0.5453 0.1597 46 640: 100%|██████████| 962/962 [04:11<00:00, 3.83it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:28<00:00, 4.85it/s]
all 8814 22077 0.703 0.766 0.727 0.462
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
20/50 14.1G 0.4716 0.5462 0.1585 48 640: 100%|██████████| 962/962 [03:48<00:00, 4.21it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.72it/s]
all 8814 22077 0.701 0.759 0.726 0.46
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
21/50 14.8G 0.4694 0.5447 0.1588 38 640: 100%|██████████| 962/962 [04:09<00:00, 3.86it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:30<00:00, 4.47it/s]
all 8814 22077 0.701 0.765 0.728 0.462
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
22/50 15G 0.47 0.5402 0.1569 48 640: 100%|██████████| 962/962 [03:50<00:00, 4.17it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.75it/s]
all 8814 22077 0.712 0.774 0.737 0.47
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
23/50 15.1G 0.4657 0.5401 0.1566 50 640: 100%|██████████| 962/962 [03:59<00:00, 4.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:25<00:00, 5.33it/s]
all 8814 22077 0.717 0.776 0.739 0.471
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
24/50 14.1G 0.4625 0.5383 0.1565 39 640: 100%|██████████| 962/962 [03:56<00:00, 4.07it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:28<00:00, 4.77it/s]
all 8814 22077 0.714 0.771 0.737 0.471
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
25/50 14.8G 0.4634 0.5397 0.1584 37 640: 100%|██████████| 962/962 [04:03<00:00, 3.95it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:25<00:00, 5.33it/s]
all 8814 22077 0.71 0.77 0.737 0.471
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
26/50 13.9G 0.4634 0.5367 0.1551 72 640: 100%|██████████| 962/962 [03:43<00:00, 4.31it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:26<00:00, 5.12it/s]
all 8814 22077 0.714 0.773 0.74 0.473
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
27/50 14.5G 0.4599 0.5339 0.1538 57 640: 100%|██████████| 962/962 [03:53<00:00, 4.12it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:27<00:00, 4.93it/s]
all 8814 22077 0.714 0.779 0.741 0.474
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
28/50 14.2G 0.4582 0.5329 0.1544 45 640: 100%|██████████| 962/962 [04:10<00:00, 3.83it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:28<00:00, 4.92it/s]
all 8814 22077 0.713 0.778 0.74 0.473
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
29/50 14.6G 0.4598 0.5323 0.1539 93 640: 100%|██████████| 962/962 [04:05<00:00, 3.91it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:28<00:00, 4.78it/s]
all 8814 22077 0.712 0.778 0.739 0.473
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
30/50 15.5G 0.4559 0.5296 0.1519 34 640: 100%|██████████| 962/962 [04:03<00:00, 3.95it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.74it/s]
all 8814 22077 0.713 0.778 0.74 0.473
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
31/50 14.7G 0.4575 0.5299 0.1531 56 640: 100%|██████████| 962/962 [04:08<00:00, 3.87it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.74it/s]
all 8814 22077 0.714 0.778 0.74 0.474
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
32/50 13.9G 0.4547 0.5297 0.1525 46 640: 100%|██████████| 962/962 [04:06<00:00, 3.91it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.63it/s]
all 8814 22077 0.713 0.777 0.739 0.474
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
33/50 14.2G 0.4557 0.5274 0.1519 56 640: 100%|██████████| 962/962 [04:15<00:00, 3.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:31<00:00, 4.42it/s]
all 8814 22077 0.713 0.778 0.74 0.475
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
34/50 14.4G 0.4532 0.5273 0.1521 47 640: 100%|██████████| 962/962 [04:18<00:00, 3.71it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:31<00:00, 4.45it/s]
all 8814 22077 0.712 0.778 0.739 0.474
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
35/50 14.2G 0.4514 0.526 0.1511 26 640: 100%|██████████| 962/962 [04:14<00:00, 3.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.71it/s]
all 8814 22077 0.714 0.78 0.74 0.475
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
36/50 14.1G 0.4521 0.5252 0.1508 54 640: 100%|██████████| 962/962 [04:14<00:00, 3.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.63it/s]
all 8814 22077 0.718 0.78 0.742 0.476
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
37/50 14.7G 0.4527 0.5261 0.1524 69 640: 100%|██████████| 962/962 [04:18<00:00, 3.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:31<00:00, 4.42it/s]
all 8814 22077 0.715 0.782 0.741 0.477
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
38/50 14.6G 0.4488 0.5262 0.1513 82 640: 100%|██████████| 962/962 [04:20<00:00, 3.70it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:30<00:00, 4.47it/s]
all 8814 22077 0.716 0.78 0.742 0.477
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
39/50 13.7G 0.4465 0.5256 0.1497 42 640: 100%|██████████| 962/962 [04:08<00:00, 3.88it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.66it/s]
all 8814 22077 0.716 0.78 0.741 0.477
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
40/50 14.9G 0.4483 0.5219 0.1483 37 640: 100%|██████████| 962/962 [04:20<00:00, 3.69it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:31<00:00, 4.41it/s]
all 8814 22077 0.717 0.78 0.742 0.478
Closing dataloader mosaic
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
41/50 14.1G 0.4184 0.4864 0.1504 31 640: 100%|██████████| 962/962 [04:07<00:00, 3.88it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:28<00:00, 4.79it/s]
all 8814 22077 0.718 0.78 0.742 0.478
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
42/50 13.5G 0.4163 0.4807 0.1498 24 640: 100%|██████████| 962/962 [04:04<00:00, 3.93it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:30<00:00, 4.47it/s]
all 8814 22077 0.719 0.78 0.742 0.478
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
43/50 14.8G 0.4152 0.4784 0.1483 17 640: 100%|██████████| 962/962 [04:14<00:00, 3.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.70it/s]
all 8814 22077 0.72 0.78 0.743 0.479
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
44/50 15.4G 0.4153 0.4774 0.148 44 640: 100%|██████████| 962/962 [04:12<00:00, 3.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.73it/s]
all 8814 22077 0.718 0.783 0.744 0.48
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
45/50 14.4G 0.4127 0.4774 0.1472 28 640: 100%|██████████| 962/962 [04:03<00:00, 3.95it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:31<00:00, 4.41it/s]
all 8814 22077 0.721 0.783 0.745 0.481
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
46/50 14.8G 0.4134 0.4767 0.1469 35 640: 100%|██████████| 962/962 [04:14<00:00, 3.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:30<00:00, 4.60it/s]
all 8814 22077 0.723 0.782 0.745 0.481
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
47/50 15G 0.4115 0.4757 0.1464 28 640: 100%|██████████| 962/962 [04:12<00:00, 3.81it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.70it/s]
all 8814 22077 0.724 0.781 0.745 0.481
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
48/50 14.9G 0.4121 0.4729 0.1465 20 640: 100%|██████████| 962/962 [04:04<00:00, 3.93it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.74it/s]
all 8814 22077 0.724 0.783 0.745 0.482
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
49/50 15G 0.4126 0.4733 0.1459 18 640: 100%|██████████| 962/962 [04:14<00:00, 3.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:29<00:00, 4.65it/s]
all 8814 22077 0.726 0.784 0.747 0.482
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
0%| | 0/962 [00:00<?, ?it/s]/home/usr1/Documents/Workspace/.venv/lib/python3.12/site-packages/torch/autograd/graph.py:824: UserWarning: grid_sampler_2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation. (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:93.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
50/50 14.8G 0.4098 0.4726 0.1457 38 640: 100%|██████████| 962/962 [04:08<00:00, 3.86it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:30<00:00, 4.54it/s]
all 8814 22077 0.725 0.783 0.746 0.482 50 epochs completed in 3.880 hours. Optimizer stripped from runs/detect/train22/weights/last.pt, 66.2MB Optimizer stripped from runs/detect/train22/weights/best.pt, 66.2MB Validating runs/detect/train22/weights/best.pt... Ultralytics 8.3.127 🚀 Python-3.12.10 torch-2.8.0.dev20250505+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32120MiB) rt-detr-l summary: 302 layers, 32,012,510 parameters, 0 gradients, 103.5 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 138/138 [00:30<00:00, 4.53it/s]
all 8814 22077 0.724 0.784 0.747 0.483
Fall-Detected 899 899 0.829 0.92 0.913 0.649
Gloves 395 858 0.84 0.906 0.915 0.471
Goggles 746 827 0.826 0.96 0.945 0.563
Hardhat 3191 8952 0.801 0.907 0.895 0.518
Ladder 193 202 0.922 0.937 0.97 0.828
Mask 292 554 0.425 0.614 0.356 0.265
NO-Gloves 571 1258 0.802 0.825 0.872 0.407
NO-Goggles 679 859 0.816 0.927 0.934 0.532
NO-Hardhat 865 2222 0.648 0.633 0.575 0.372
NO-Mask 327 505 0.563 0.63 0.443 0.302
NO-Safety Vest 189 361 0.34 0.423 0.285 0.153
Person 193 277 0.914 0.935 0.945 0.799
Safety Cone 338 3016 0.79 0.649 0.704 0.388
Safety Vest 609 1287 0.619 0.715 0.7 0.509
Speed: 0.0ms preprocess, 2.1ms inference, 0.0ms loss, 0.1ms postprocess per image
Results saved to runs/detect/train22
Training Finished
Training results: ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7fed551b3830>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
curves_results: [[array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,
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[ 0.98791, 0.98791, 0.98791, ..., 0, 0, 0],
...,
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[ 0.8309, 0.8309, 0.8309, ..., 0, 0, 0],
[ 0.95882, 0.95882, 0.95882, ..., 0, 0, 0]], shape=(14, 1000)), 'Confidence', 'Recall']]
fitness: np.float64(0.5089386638539161)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.64851, 0.47112, 0.56289, 0.51762, 0.82785, 0.26538, 0.40727, 0.53245, 0.37162, 0.30202, 0.15281, 0.79919, 0.38786, 0.50876])
names: {0: 'Fall-Detected', 1: 'Gloves', 2: 'Goggles', 3: 'Hardhat', 4: 'Ladder', 5: 'Mask', 6: 'NO-Gloves', 7: 'NO-Goggles', 8: 'NO-Hardhat', 9: 'NO-Mask', 10: 'NO-Safety Vest', 11: 'Person', 12: 'Safety Cone', 13: 'Safety Vest'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.7241194116399247), 'metrics/recall(B)': np.float64(0.7842830401778953), 'metrics/mAP50(B)': np.float64(0.7466592039862382), 'metrics/mAP50-95(B)': np.float64(0.48252527050588023), 'fitness': np.float64(0.5089386638539161)}
save_dir: PosixPath('runs/detect/train22')
speed: {'preprocess': 0.04495023837362547, 'inference': 2.097866742004104, 'loss': 5.6560583550433114e-05, 'postprocess': 0.12036657521851539}
task: 'detect'
Total duration: 233.4422304670016
Model 6 RT-DETR-L with Augmentation Results: Loss and per class precision-recall curve¶
Example real time inference¶
In [ ]:
# USING YOLO MODEL
video = '/home/usr1/Documents/Workspace/ppe safety.mp4'
best_model = YOLO("/home/usr1/Documents/Workspace/runs/detect/train31/weights/best.pt")
results = model(best_model)
WARNING ⚠️
inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory
errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.
Example:
results = model(source=..., stream=True) # generator of Results objects
for r in results:
boxes = r.boxes # Boxes object for bbox outputs
masks = r.masks # Masks object for segment masks outputs
probs = r.probs # Class probabilities for classification outputs
video 1/1 (frame 1/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 16.9ms
video 1/1 (frame 2/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 3/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 4/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 5/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 6/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 7/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 8/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 10/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 11/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 12/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 13/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 14/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 15/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 16/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 17/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 18/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 19/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 20/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 21/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 22/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 23/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 24/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 25/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 26/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 27/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 28/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 29/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 30/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 31/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 32/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 33/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 34/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 35/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 36/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 37/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 38/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 39/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 40/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 41/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 42/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 43/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 44/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 45/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
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video 1/1 (frame 1046/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 3.0ms
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video 1/1 (frame 3167/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
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video 1/1 (frame 3173/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.4ms
video 1/1 (frame 3174/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 3175/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
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video 1/1 (frame 3185/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
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video 1/1 (frame 3187/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
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video 1/1 (frame 3308/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 2.9ms
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video 1/1 (frame 3495/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
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video 1/1 (frame 3500/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 3501/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.1ms
video 1/1 (frame 3502/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 3503/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 3504/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3505/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 3506/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 3507/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 2.9ms
video 1/1 (frame 3508/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 3509/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 2.9ms
video 1/1 (frame 3510/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.1ms
video 1/1 (frame 3511/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 3512/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 3513/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 3514/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 3515/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 3516/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.3ms
video 1/1 (frame 3517/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 3518/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 3519/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 3520/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3521/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3522/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 3523/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 3524/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3525/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3526/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3527/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3528/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 3529/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3530/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3531/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 3532/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.1ms
video 1/1 (frame 3533/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 3534/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3535/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 3536/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3537/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 3538/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 2.9ms
video 1/1 (frame 3539/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 3540/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 3541/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 3542/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 2.9ms
video 1/1 (frame 3543/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 3544/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.1ms
video 1/1 (frame 3545/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 3546/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 3547/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 3548/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.6ms
video 1/1 (frame 3549/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 3550/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3551/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.5ms
video 1/1 (frame 3552/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 3553/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3554/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3555/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3556/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3557/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3558/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 3559/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.2ms
video 1/1 (frame 3560/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 3561/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3562/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 3563/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 3564/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3565/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3566/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 3567/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3568/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3569/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 3570/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3571/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3572/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3573/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 3574/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3575/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3576/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 3577/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3578/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3579/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3580/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3581/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3582/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 3583/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 3584/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3585/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3586/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3587/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3588/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3589/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 3590/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3591/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3592/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 3593/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 3594/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 2.9ms
video 1/1 (frame 3595/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3596/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3597/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3598/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3599/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3600/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3601/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
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video 1/1 (frame 3604/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 3.6ms
video 1/1 (frame 3605/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 3.1ms
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video 1/1 (frame 3608/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3609/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3610/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 3611/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3612/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3613/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 3614/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 3615/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3616/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 3617/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3618/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3619/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 3620/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3621/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3622/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3623/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 3624/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3625/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 3626/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 3627/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 3628/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3629/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3630/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3631/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 3632/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
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video 1/1 (frame 4070/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Fall-Detected, 3.0ms
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video 1/1 (frame 4234/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 2.9ms
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video 1/1 (frame 4240/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
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video 1/1 (frame 4243/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 2.9ms
video 1/1 (frame 4244/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 2.9ms
video 1/1 (frame 4245/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 2 Safety Vests, 3.0ms
video 1/1 (frame 4246/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 2 Safety Vests, 3.4ms
video 1/1 (frame 4247/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.1ms
video 1/1 (frame 4248/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 4249/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 4250/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 4251/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 4252/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
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video 1/1 (frame 4255/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 4.1ms
video 1/1 (frame 4256/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 4257/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 4258/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 4259/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
video 1/1 (frame 4260/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 4261/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 4262/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 4263/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 2.9ms
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video 1/1 (frame 4266/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 2.9ms
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video 1/1 (frame 4271/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
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video 1/1 (frame 4273/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 4274/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 4275/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 4276/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 4277/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 4278/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
video 1/1 (frame 4279/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
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video 1/1 (frame 5764/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
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video 1/1 (frame 5780/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
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video 1/1 (frame 5782/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 5783/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 5784/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.0ms
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video 1/1 (frame 5841/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Person, 3.5ms
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video 1/1 (frame 5844/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.0ms
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video 1/1 (frame 6206/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Safety Vest, 3.7ms
video 1/1 (frame 6207/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.4ms
video 1/1 (frame 6208/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.0ms
video 1/1 (frame 6209/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.0ms
video 1/1 (frame 6210/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.3ms
video 1/1 (frame 6211/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.0ms
video 1/1 (frame 6212/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.8ms
video 1/1 (frame 6213/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 1 Ladder, 1 Person, 4.0ms
video 1/1 (frame 6214/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Safety Vest, 3.0ms
video 1/1 (frame 6215/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.5ms
video 1/1 (frame 6216/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.2ms
video 1/1 (frame 6217/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Safety Vest, 3.2ms
video 1/1 (frame 6218/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Safety Vest, 3.7ms
video 1/1 (frame 6219/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Safety Vest, 3.9ms
video 1/1 (frame 6220/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.7ms
video 1/1 (frame 6221/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.3ms
video 1/1 (frame 6222/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.2ms
video 1/1 (frame 6223/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.0ms
video 1/1 (frame 6224/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.3ms
video 1/1 (frame 6225/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.0ms
video 1/1 (frame 6226/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.0ms
video 1/1 (frame 6227/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.2ms
video 1/1 (frame 6228/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.1ms
video 1/1 (frame 6229/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.5ms
video 1/1 (frame 6230/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.2ms
video 1/1 (frame 6231/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.0ms
video 1/1 (frame 6232/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.5ms
video 1/1 (frame 6233/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 2 Ladders, 1 Person, 3.0ms
video 1/1 (frame 6234/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.0ms
video 1/1 (frame 6235/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.2ms
video 1/1 (frame 6236/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.1ms
video 1/1 (frame 6237/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 6238/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 2 Ladders, 1 Person, 3.9ms
video 1/1 (frame 6239/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.1ms
video 1/1 (frame 6240/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.4ms
video 1/1 (frame 6241/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Person, 3.0ms
video 1/1 (frame 6242/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Person, 3.3ms
video 1/1 (frame 6243/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Person, 3.0ms
video 1/1 (frame 6244/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Person, 3.5ms
video 1/1 (frame 6245/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Person, 3.1ms
video 1/1 (frame 6246/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.1ms
video 1/1 (frame 6247/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Person, 3.1ms
video 1/1 (frame 6248/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.1ms
video 1/1 (frame 6249/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 1 Safety Vest, 3.0ms
video 1/1 (frame 6250/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6251/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6252/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6253/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6254/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6255/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6256/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 6257/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 6258/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6259/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 6260/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6261/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6262/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 6263/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6264/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Person, 3.0ms
video 1/1 (frame 6265/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Person, 3.4ms
video 1/1 (frame 6266/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 6267/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 6268/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 6269/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.1ms
video 1/1 (frame 6270/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 6271/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 6272/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.0ms
video 1/1 (frame 6273/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6274/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 6275/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6276/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.0ms
video 1/1 (frame 6277/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6278/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6279/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6280/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6281/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.6ms
video 1/1 (frame 6282/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 6283/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.3ms
video 1/1 (frame 6284/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.1ms
video 1/1 (frame 6285/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.0ms
video 1/1 (frame 6286/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6287/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6288/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.8ms
video 1/1 (frame 6289/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 6290/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.1ms
video 1/1 (frame 6291/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6292/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6293/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 6294/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 6295/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6296/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.0ms
video 1/1 (frame 6297/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.1ms
video 1/1 (frame 6298/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6299/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.0ms
video 1/1 (frame 6300/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.0ms
video 1/1 (frame 6301/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 6302/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.1ms
video 1/1 (frame 6303/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6304/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Ladder, 3.0ms
video 1/1 (frame 6305/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6306/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6307/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6308/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 6309/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6310/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 6311/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6312/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6313/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6314/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 6315/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 6316/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 6317/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 6318/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6319/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 6320/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6321/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6322/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6323/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6324/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6325/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 6326/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6327/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6328/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 6329/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6330/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6331/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6332/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 6333/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 6334/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6335/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
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video 1/1 (frame 6337/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6338/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6339/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6340/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 6341/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6342/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 6343/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 6344/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6345/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6346/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 6347/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6348/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 6349/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 6350/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 6351/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
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video 1/1 (frame 6355/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 6356/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Cone, 3.0ms
video 1/1 (frame 6357/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 6358/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
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video 1/1 (frame 6383/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
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video 1/1 (frame 6385/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.5ms
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video 1/1 (frame 7893/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 7894/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 7895/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.4ms
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video 1/1 (frame 7897/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 7898/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 7899/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.6ms
video 1/1 (frame 7900/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 7901/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 7902/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 7903/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 7904/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
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video 1/1 (frame 7906/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 7907/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Cone, 3.4ms
video 1/1 (frame 7908/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Cone, 3.2ms
video 1/1 (frame 7909/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 7910/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 7911/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 7912/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 7913/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 7914/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 7915/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.6ms
video 1/1 (frame 7916/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 7917/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 7918/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 7919/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 7920/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 7921/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 7922/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.0ms
video 1/1 (frame 7923/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 7924/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 7925/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 7926/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 7927/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 7928/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 7929/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 7930/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 7931/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 7932/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 7933/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 7934/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 7935/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Cone, 3.2ms
video 1/1 (frame 7936/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 7937/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 7938/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
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video 1/1 (frame 7941/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 7942/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 7943/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 7944/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 7945/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 7946/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
video 1/1 (frame 7947/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.1ms
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video 1/1 (frame 7949/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 7950/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
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video 1/1 (frame 8291/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.7ms
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video 1/1 (frame 8931/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 2 Masks, 3.4ms
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video 1/1 (frame 8973/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.7ms
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video 1/1 (frame 8977/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
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video 1/1 (frame 8981/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 8982/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 8983/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.6ms
video 1/1 (frame 8984/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 8985/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
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video 1/1 (frame 9592/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9593/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.8ms
video 1/1 (frame 9594/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.3ms
video 1/1 (frame 9595/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.0ms
video 1/1 (frame 9596/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.7ms
video 1/1 (frame 9597/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.8ms
video 1/1 (frame 9598/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 9599/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9600/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9601/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.7ms
video 1/1 (frame 9602/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.5ms
video 1/1 (frame 9603/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.5ms
video 1/1 (frame 9604/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.3ms
video 1/1 (frame 9605/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.7ms
video 1/1 (frame 9606/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 9607/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.2ms
video 1/1 (frame 9608/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.5ms
video 1/1 (frame 9609/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.9ms
video 1/1 (frame 9610/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9611/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 9612/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9613/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.1ms
video 1/1 (frame 9614/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 9615/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9616/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9617/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.9ms
video 1/1 (frame 9618/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.6ms
video 1/1 (frame 9619/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9620/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.8ms
video 1/1 (frame 9621/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.6ms
video 1/1 (frame 9622/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.3ms
video 1/1 (frame 9623/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.9ms
video 1/1 (frame 9624/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.1ms
video 1/1 (frame 9625/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.3ms
video 1/1 (frame 9626/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.6ms
video 1/1 (frame 9627/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.6ms
video 1/1 (frame 9628/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.3ms
video 1/1 (frame 9629/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.3ms
video 1/1 (frame 9630/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9631/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.4ms
video 1/1 (frame 9632/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.5ms
video 1/1 (frame 9633/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.9ms
video 1/1 (frame 9634/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 9635/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.9ms
video 1/1 (frame 9636/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9637/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.7ms
video 1/1 (frame 9638/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9639/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.5ms
video 1/1 (frame 9640/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9641/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9642/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.5ms
video 1/1 (frame 9643/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.9ms
video 1/1 (frame 9644/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 9645/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9646/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9647/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 2 Safety Vests, 3.6ms
video 1/1 (frame 9648/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.5ms
video 1/1 (frame 9649/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 2 Safety Vests, 3.3ms
video 1/1 (frame 9650/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.2ms
video 1/1 (frame 9651/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 9652/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.3ms
video 1/1 (frame 9653/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9654/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9655/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9656/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.8ms
video 1/1 (frame 9657/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9658/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.3ms
video 1/1 (frame 9659/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.2ms
video 1/1 (frame 9660/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.7ms
video 1/1 (frame 9661/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 9662/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9663/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.6ms
video 1/1 (frame 9664/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.9ms
video 1/1 (frame 9665/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9666/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9667/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9668/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9669/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 9670/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 9671/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9672/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 9673/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 9674/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.6ms
video 1/1 (frame 9675/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9676/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9677/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 9678/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 9679/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 9680/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 9681/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 9682/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9683/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9684/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 9685/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 9686/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 9687/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 9688/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 9689/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9690/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 9691/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9692/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.2ms
video 1/1 (frame 9693/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 9694/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9695/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 9696/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 9697/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9698/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 9699/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9700/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9701/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9702/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9703/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9704/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 9705/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 9706/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 9707/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 9708/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 9709/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 9710/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 9711/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9712/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 9713/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 9714/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9715/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9716/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 9717/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 9718/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9719/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9720/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9721/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9722/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9723/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 9724/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 9725/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9726/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 9727/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9728/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.8ms
video 1/1 (frame 9729/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9730/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9731/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.2ms
video 1/1 (frame 9732/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.4ms
video 1/1 (frame 9733/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 9734/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 9735/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
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video 1/1 (frame 11066/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 1 Safety Vest, 3.5ms
video 1/1 (frame 11067/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 1 Safety Vest, 4.7ms
video 1/1 (frame 11068/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11069/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11070/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11071/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11072/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11073/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11074/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11075/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11076/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11077/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11078/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.7ms
video 1/1 (frame 11079/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11080/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11081/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11082/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11083/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11084/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11085/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11086/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11087/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11088/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11089/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11090/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11091/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.6ms
video 1/1 (frame 11092/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.7ms
video 1/1 (frame 11093/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11094/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11095/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11096/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11097/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11098/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11099/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11100/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11101/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11102/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11103/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11104/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11105/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11106/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.5ms
video 1/1 (frame 11107/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.5ms
video 1/1 (frame 11108/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11109/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11110/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11111/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11112/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11113/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11114/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11115/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11116/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11117/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11118/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11119/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11120/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11121/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11122/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.8ms
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video 1/1 (frame 11130/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
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video 1/1 (frame 11132/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11133/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11134/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11135/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11136/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 11137/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11138/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11139/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.4ms
video 1/1 (frame 11140/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.8ms
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video 1/1 (frame 11380/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11381/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11382/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11383/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11384/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11385/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.2ms
video 1/1 (frame 11386/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 11387/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.9ms
video 1/1 (frame 11388/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11389/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11390/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11391/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11392/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.2ms
video 1/1 (frame 11393/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11394/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11395/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11396/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11397/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11398/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.7ms
video 1/1 (frame 11399/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11400/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11401/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11402/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11403/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11404/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11405/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11406/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.5ms
video 1/1 (frame 11407/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11408/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11409/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11410/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.6ms
video 1/1 (frame 11411/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 11412/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11413/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11414/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.6ms
video 1/1 (frame 11415/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11416/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11417/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11418/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11419/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11420/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11421/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11422/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.8ms
video 1/1 (frame 11423/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11424/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11425/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11426/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11427/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11428/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11429/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11430/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11431/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11432/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11433/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11434/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11435/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11436/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11437/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11438/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11439/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11440/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11441/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11442/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11443/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11444/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11445/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11446/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.8ms
video 1/1 (frame 11447/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11448/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11449/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11450/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.4ms
video 1/1 (frame 11451/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11452/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11453/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11454/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11455/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11456/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11457/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11458/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11459/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11460/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11461/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.2ms
video 1/1 (frame 11462/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.9ms
video 1/1 (frame 11463/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.5ms
video 1/1 (frame 11464/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11465/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11466/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11467/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.7ms
video 1/1 (frame 11468/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11469/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11470/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11471/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11472/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11473/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11474/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11475/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.2ms
video 1/1 (frame 11476/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11477/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11478/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11479/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 11480/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11481/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11482/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11483/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.7ms
video 1/1 (frame 11484/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11485/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11486/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11487/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11488/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11489/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11490/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11491/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.2ms
video 1/1 (frame 11492/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 11493/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11494/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11495/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11496/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11497/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11498/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11499/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11500/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11501/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11502/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11503/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11504/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11505/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11506/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11507/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11508/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11509/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 11510/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11511/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.4ms
video 1/1 (frame 11512/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11513/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11514/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11515/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.5ms
video 1/1 (frame 11516/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11517/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11518/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11519/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11520/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11521/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11522/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11523/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11524/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11525/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11526/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.4ms
video 1/1 (frame 11527/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11528/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11529/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11530/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11531/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 11532/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11533/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11534/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11535/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11536/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11537/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11538/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11539/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11540/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11541/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11542/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11543/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11544/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11545/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11546/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
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video 1/1 (frame 11558/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11559/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11560/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11561/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
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video 1/1 (frame 11563/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
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video 1/1 (frame 11565/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11566/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11567/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11568/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11569/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11570/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11571/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11572/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11573/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11574/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11575/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 11576/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11577/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11578/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11579/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11580/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11581/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11582/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11583/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11584/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11585/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11586/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 11587/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11588/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11589/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11590/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11591/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11592/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11593/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11594/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11595/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11596/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11597/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.7ms
video 1/1 (frame 11598/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11599/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11600/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11601/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.8ms
video 1/1 (frame 11602/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11603/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11604/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11605/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11606/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11607/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11608/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11609/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11610/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11611/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11612/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11613/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11614/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11615/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11616/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11617/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.6ms
video 1/1 (frame 11618/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11619/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11620/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11621/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11622/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11623/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11624/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11625/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11626/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11627/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11628/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11629/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.8ms
video 1/1 (frame 11630/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.6ms
video 1/1 (frame 11631/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11632/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11633/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11634/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11635/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11636/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11637/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11638/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11639/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11640/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11641/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.6ms
video 1/1 (frame 11642/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11643/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11644/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11645/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.1ms
video 1/1 (frame 11646/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.4ms
video 1/1 (frame 11647/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11648/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11649/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11650/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11651/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11652/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 11653/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11654/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11655/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.8ms
video 1/1 (frame 11656/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11657/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11658/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11659/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11660/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11661/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.5ms
video 1/1 (frame 11662/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 11663/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.4ms
video 1/1 (frame 11664/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11665/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11666/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11667/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11668/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11669/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11670/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11671/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11672/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11673/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.6ms
video 1/1 (frame 11674/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 11675/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11676/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11677/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.8ms
video 1/1 (frame 11678/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11679/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 11680/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11681/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11682/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.4ms
video 1/1 (frame 11683/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11684/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11685/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11686/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.9ms
video 1/1 (frame 11687/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11688/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11689/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11690/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 11691/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11692/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11693/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11694/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.9ms
video 1/1 (frame 11695/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11696/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 11697/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11698/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11699/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 11700/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 11701/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 11702/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 11703/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11704/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 NO-Safety Vest, 3.4ms
video 1/1 (frame 11705/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 11706/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 4.6ms
video 1/1 (frame 11707/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 4.0ms
video 1/1 (frame 11708/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11709/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.1ms
video 1/1 (frame 11710/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.2ms
video 1/1 (frame 11711/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 11712/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 3.9ms
video 1/1 (frame 11713/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 4.5ms
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video 1/1 (frame 11717/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 1 NO-Safety Vest, 4.0ms
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video 1/1 (frame 11719/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 3.6ms
video 1/1 (frame 11720/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 4.4ms
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video 1/1 (frame 11726/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 3.4ms
video 1/1 (frame 11727/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 3.6ms
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video 1/1 (frame 11738/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 4.0ms
video 1/1 (frame 11739/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 3.4ms
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video 1/1 (frame 11743/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 1 NO-Safety Vest, 3.3ms
video 1/1 (frame 11744/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Hardhat, 3.9ms
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video 1/1 (frame 11935/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.5ms
video 1/1 (frame 11936/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
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video 1/1 (frame 11952/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 2 Safety Vests, 3.9ms
video 1/1 (frame 11953/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 2 Safety Vests, 3.8ms
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video 1/1 (frame 11959/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.5ms
video 1/1 (frame 11960/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.3ms
video 1/1 (frame 11961/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.0ms
video 1/1 (frame 11962/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 11963/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 11964/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 2 Safety Vests, 3.6ms
video 1/1 (frame 11965/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.5ms
video 1/1 (frame 11966/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 2 Safety Vests, 3.7ms
video 1/1 (frame 11967/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.1ms
video 1/1 (frame 11968/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 2 Safety Vests, 3.8ms
video 1/1 (frame 11969/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 11970/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.3ms
video 1/1 (frame 11971/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.3ms
video 1/1 (frame 11972/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 2 Safety Vests, 3.6ms
video 1/1 (frame 11973/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.5ms
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video 1/1 (frame 11977/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.7ms
video 1/1 (frame 11978/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 11979/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 11980/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.3ms
video 1/1 (frame 11981/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 11982/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.3ms
video 1/1 (frame 11983/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.1ms
video 1/1 (frame 11984/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 11985/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.9ms
video 1/1 (frame 11986/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.6ms
video 1/1 (frame 11987/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.6ms
video 1/1 (frame 11988/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.8ms
video 1/1 (frame 11989/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.7ms
video 1/1 (frame 11990/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 11991/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.5ms
video 1/1 (frame 11992/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.3ms
video 1/1 (frame 11993/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.2ms
video 1/1 (frame 11994/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 11995/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.6ms
video 1/1 (frame 11996/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.5ms
video 1/1 (frame 11997/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 4.9ms
video 1/1 (frame 11998/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 1 Safety Vest, 3.4ms
video 1/1 (frame 11999/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 12000/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 12001/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 12002/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12003/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 12004/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 12005/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 12006/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12007/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.2ms
video 1/1 (frame 12008/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.8ms
video 1/1 (frame 12009/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 12010/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
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video 1/1 (frame 12013/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 12014/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 12015/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 12016/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 12017/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.2ms
video 1/1 (frame 12018/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 12019/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 12020/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 12021/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 12022/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12023/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12024/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 12025/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 12026/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.5ms
video 1/1 (frame 12027/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 12028/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 12029/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 12030/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 12031/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 12032/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 12033/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12034/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 12035/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 12036/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12037/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 12038/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 12039/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 12040/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 12041/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 12042/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 12043/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 12044/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 12045/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 12046/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 12047/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 12048/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.4ms
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video 1/1 (frame 12063/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
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video 1/1 (frame 12828/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.9ms
video 1/1 (frame 12829/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 12830/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 12831/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 12832/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 12833/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 12834/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12835/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.1ms
video 1/1 (frame 12836/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 12837/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 12838/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 12839/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 12840/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 12841/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 12842/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 12843/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 12844/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.2ms
video 1/1 (frame 12845/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 12846/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12847/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12848/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12849/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.4ms
video 1/1 (frame 12850/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.2ms
video 1/1 (frame 12851/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 12852/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 12853/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.7ms
video 1/1 (frame 12854/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 12855/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12856/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 12857/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 12858/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 12859/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 12860/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 12861/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12862/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
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video 1/1 (frame 12864/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12865/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 12866/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 12867/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.5ms
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video 1/1 (frame 12869/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
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video 1/1 (frame 12892/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 12893/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 12894/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.9ms
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video 1/1 (frame 12904/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
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video 1/1 (frame 12917/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.3ms
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video 1/1 (frame 12919/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 12920/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 12921/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
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video 1/1 (frame 12923/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12924/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.5ms
video 1/1 (frame 12925/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 12926/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.4ms
video 1/1 (frame 12927/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
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video 1/1 (frame 12937/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
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video 1/1 (frame 13194/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 13195/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 13196/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 13197/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 13198/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 13199/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 13200/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 13201/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 13202/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 13203/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.9ms
video 1/1 (frame 13204/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 13205/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 13206/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 13207/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 13208/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 13209/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 13210/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 13211/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 13212/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 13213/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 13214/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 6.4ms
video 1/1 (frame 13215/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 13216/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 13217/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 13218/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 13219/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 13220/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 13221/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 13222/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 13223/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.5ms
video 1/1 (frame 13224/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 13225/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 13226/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 13227/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.7ms
video 1/1 (frame 13228/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.9ms
video 1/1 (frame 13229/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.4ms
video 1/1 (frame 13230/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 13231/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 13232/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 13233/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 13234/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 13235/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.1ms
video 1/1 (frame 13236/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 13237/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 13238/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 13239/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.2ms
video 1/1 (frame 13240/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.9ms
video 1/1 (frame 13241/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 13242/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 13243/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 13244/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 13245/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 13246/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 13247/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 13248/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.3ms
video 1/1 (frame 13249/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 13250/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.5ms
video 1/1 (frame 13251/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 13252/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.4ms
video 1/1 (frame 13253/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.2ms
video 1/1 (frame 13254/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 5.0ms
video 1/1 (frame 13255/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
video 1/1 (frame 13256/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.8ms
video 1/1 (frame 13257/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 13258/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.3ms
video 1/1 (frame 13259/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.7ms
video 1/1 (frame 13260/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 3.6ms
video 1/1 (frame 13261/13261) /home/usr1/Documents/Workspace/ppe safety.mp4: 384x640 (no detections), 4.0ms
Speed: 1.2ms preprocess, 3.4ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)