python train.py --data coco128.yaml --epochs 300 --weights yolov5m6 --cfg yolov5m.yaml --batch-size 32Training Logs
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
0%| | 0/4 [00:00<?, ?it/s]train.py:323: FutureWarning: Non-finite norm encountered in torch.nn.utils.clip_grad_norm_; continuing anyway. Note that the default behavior will change in a future release to error out if a non-finite total norm is encountered. At that point, setting error_if_nonfinite=false will be required to retain the old behavior.
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients
0/299 2.51G 0.11 0.06781 0.106 455 640: 100%|██████████| 4/4 [00:40<00:00, 10.13s/it]
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 2/2 [00:00<00:00, 4.67it/s]
all 128 929 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
1/299 6.11G 0.1099 0.07716 0.1061 380 640: 100%|██████████| 4/4 [00:01<00:00, 2.62it/s]
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 2/2 [00:00<00:00, 4.39it/s]
all 128 929 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
2/299 6.11G 0.1107 0.07112 0.1065 457 640: 100%|██████████| 4/4 [00:01<00:00, 2.63it/s]
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 2/2 [00:00<00:00, 4.31it/s]
all 128 929 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
3/299 6.11G 0.1098 0.08129 0.1063 452 640: 100%|██████████| 4/4 [00:01<00:00, 2.62it/s]
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 2/2 [00:00<00:00, 4.50it/s]
all 128 929 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
4/299 6.11G 0.1091 0.0742 0.1061 385 640: 100%|██████████| 4/4 [00:01<00:00, 2.85it/s]
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 2/2 [00:00<00:00, 4.24it/s]
all 128 929 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
5/299 6.11G 0.1091 0.07433 0.1059 452 640: 100%|██████████| 4/4 [00:01<00:00, 2.79it/s]
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 2/2 [00:00<00:00, 4.46it/s]
all 128 929 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
6/299 6.11G 0.108 0.0719 0.105 449 640: 100%|██████████| 4/4 [00:01<00:00, 2.57it/s]
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 2/2 [00:00<00:00, 3.87it/s]
all 128 929 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
7/299 6.11G 0.1066 0.07407 0.1049 455 640: 100%|██████████| 4/4 [00:01<00:00, 2.65it/s]
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 2/2 [00:00<00:00, 4.52it/s]
all 128 929 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
8/299 6.11G 0.106 0.07617 0.1044 413 640: 100%|██████████| 4/4 [00:01<00:00, 2.58it/s]
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 2/2 [00:00<00:00, 4.58it/s]
all 128 929 0 0 0 0
python train.py --data FlickrLogos-v2.yaml --epochs 300 --weights yolov5n --cfg yolov5n.yaml --batch-size 32
python segment/train.py --data LogoRec.yaml --epochs 300 --weights yolov5n-seg.pt --batch-size 32