# Train
python train.py --data VOC.yaml --weights "" --cfg yolov2_voc.yaml --img 640 --device 0 --yolov2loss
# Eval
# python3 val.py --weights runs/train/voc/exp/weights/best.pt --data VOC.yaml --img 640 --device 0
Fusing layers...
yolov2_voc summary: 53 layers, 50645053 parameters, 0 gradients, 69.5 GFLOPs
val: Scanning /workdir/datasets/VOC/labels/test2007.cache... 4952 images, 0 backgrounds, 0 corrupt: 100%|██████████| 4952/4952 00:00
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 155/155 00:41
all 4952 12032 0.735 0.711 0.751 0.443
aeroplane 4952 285 0.664 0.702 0.705 0.372
bicycle 4952 337 0.862 0.814 0.864 0.526
bird 4952 459 0.779 0.636 0.729 0.39
boat 4952 263 0.618 0.593 0.622 0.305
bottle 4952 469 0.783 0.516 0.613 0.319
bus 4952 213 0.798 0.808 0.857 0.597
car 4952 1201 0.814 0.836 0.859 0.554
cat 4952 358 0.707 0.811 0.776 0.477
chair 4952 756 0.642 0.557 0.628 0.341
cow 4952 244 0.748 0.758 0.789 0.489
diningtable 4952 206 0.583 0.7 0.65 0.388
dog 4952 489 0.739 0.753 0.8 0.486
horse 4952 348 0.822 0.835 0.874 0.539
motorbike 4952 325 0.808 0.751 0.831 0.498
person 4952 4528 0.843 0.772 0.848 0.484
pottedplant 4952 480 0.695 0.418 0.503 0.226
sheep 4952 242 0.73 0.682 0.758 0.467
sofa 4952 239 0.584 0.741 0.735 0.458
train 4952 282 0.727 0.839 0.825 0.477
tvmonitor 4952 308 0.748 0.701 0.763 0.466
Speed: 0.1ms pre-process, 3.1ms inference, 1.3ms NMS per image at shape (32, 3, 640, 640)# Train
python train.py --data VOC.yaml --weights "" --cfg yolov2-fast_voc.yaml --img 640 --device 0 --yolov2loss
# Eval
# python3 val.py --weights runs/train/voc/exp3/weights/best.pt --data VOC.yaml --img 640 --device 0
Fusing layers...
yolov2-fast_voc summary: 33 layers, 42367485 parameters, 0 gradients, 48.5 GFLOPs
val: Scanning /workdir/datasets/VOC/labels/test2007.cache... 4952 images, 0 backgrounds, 0 corrupt: 100%|██████████| 4952/4952 00:00
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 155/155 00:37
all 4952 12032 0.626 0.612 0.626 0.298
aeroplane 4952 285 0.551 0.621 0.61 0.226
bicycle 4952 337 0.721 0.713 0.765 0.401
bird 4952 459 0.692 0.494 0.577 0.255
boat 4952 263 0.533 0.491 0.492 0.199
bottle 4952 469 0.712 0.373 0.45 0.208
bus 4952 213 0.6 0.732 0.663 0.342
car 4952 1201 0.649 0.742 0.712 0.365
cat 4952 358 0.454 0.693 0.605 0.253
chair 4952 756 0.635 0.439 0.52 0.268
cow 4952 244 0.641 0.611 0.672 0.342
diningtable 4952 206 0.521 0.573 0.539 0.206
dog 4952 489 0.54 0.642 0.63 0.282
horse 4952 348 0.661 0.773 0.789 0.401
motorbike 4952 325 0.635 0.714 0.731 0.364
person 4952 4528 0.75 0.707 0.76 0.371
pottedplant 4952 480 0.663 0.352 0.434 0.173
sheep 4952 242 0.752 0.607 0.692 0.386
sofa 4952 239 0.493 0.598 0.554 0.263
train 4952 282 0.545 0.766 0.654 0.271
tvmonitor 4952 308 0.769 0.597 0.668 0.381
Speed: 0.1ms pre-process, 2.3ms inference, 1.5ms NMS per image at shape (32, 3, 640, 640)# Train
python train.py --data coco.yaml --weights "" --cfg yolov2.yaml --img 640 --device 0 --yolov2loss
# Eval
# python3 val.py --weights runs/train/coco/exp/weights/best.pt --data coco.yaml --img 640 --device 0
Fusing layers...
yolov2 summary: 53 layers, 50952553 parameters, 0 gradients, 69.7 GFLOPs
val: Scanning /workdir/datasets/coco/val2017.cache... 4952 images, 48 backgrounds, 0 corrupt: 100%|██████████| 5000/5000 00:00
Class Images Instances P R mAP50 mAP50-95: 1%| | 1/157 00:01WARNING ⚠️ NMS time limit 2.100s exceeded
Class Images Instances P R mAP50 mAP50-95: 2%|▏ | 3/157 00:08WARNING ⚠️ NMS time limit 2.100s exceeded
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 157/157 00:57
all 5000 36335 0.627 0.48 0.507 0.286
Speed: 0.1ms pre-process, 3.1ms inference, 2.0ms NMS per image at shape (32, 3, 640, 640)# Train
python3 train.py --data coco.yaml --weights "" --cfg yolov2-fast.yaml --img 640 --device 0 --yolov2loss
# Eval
# python3 val.py --weights runs/train/coco/exp2/weights/best.pt --data coco.yaml --img 640 --device 0
Fusing layers...
yolov2-fast summary: 33 layers, 42674985 parameters, 0 gradients, 48.8 GFLOPs
val: Scanning /workdir/datasets/coco/val2017.cache... 4952 images, 48 backgrounds, 0 corrupt: 100%|██████████| 5000/5000 00:00
Class Images Instances P R mAP50 mAP50-95: 1%| | 1/157 00:01WARNING ⚠️ NMS time limit 2.100s exceeded
Class Images Instances P R mAP50 mAP50-95: 1%|▏ | 2/157 00:04WARNING ⚠️ NMS time limit 2.100s exceeded
Class Images Instances P R mAP50 mAP50-95: 2%|▏ | 3/157 00:07WARNING ⚠️ NMS time limit 2.100s exceeded
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 157/157 00:53
all 5000 36335 0.549 0.402 0.412 0.201
Speed: 0.1ms pre-process, 2.4ms inference, 2.1ms NMS per image at shape (32, 3, 640, 640)