Transferred 557/566 items from weights/yolov7_training.pt
Scaled weight_decay = 0.0005
Optimizer groups: 95 .bias, 95 conv.weight, 98 other
train: Scanning '..\train\labels.cache' images and labels... 7566 found, 0 missing, 81 empty, 0 corrupted: 100%|██████████| 7566/7566 [00:00<?, ?it/s]
val: Scanning '..\valid\labels.cache' images and labels... 805 found, 0 missing, 11 empty, 0 corrupted: 100%|██████████| 805/805 [00:00<?, ?it/s]
autoanchor: Analyzing anchors... anchors/target = 4.71, Best Possible Recall (BPR) = 0.9997
Image sizes 640 train, 640 test
Using 5 dataloader workers
Logging results to runs\train\exp11
Starting training for 200 epochs...
Epoch gpu_mem box obj cls total labels img_size
0/199 8.92G 0.05505 0.02018 0.01432 0.08955 120 640: 100%|██████████| 473/473 [1:19:46<00:00, 10.12s/it]
Class Images Labels P R mAP@.5 mAP@.5:.95: 0%| | 0/26 [00:00<?, ?it/s]D:\Users\Lenovo\anaconda3\lib\site-packages\torch\functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\TensorShape.cpp:2895.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Class Images Labels P R mAP@.5 mAP@.5:.95: 0%| | 0/26 [00:05<?, ?it/s]
Traceback (most recent call last):
File "E:\vehicle\yolov7\train.py", line 620, in <module>
train(hyp, opt, device, tb_writer)
File "E:\vehicle\yolov7\train.py", line 416, in train
results, maps, times = test.test(data_dict,
File "E:\vehicle\yolov7\test.py", line 119, in test
loss += compute_loss([x.float() for x in train_out], targets)[1][:3] # box, obj, cls
File "E:\vehicle\yolov7\utils\loss.py", line 469, in __call__
iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, EIoU=True) # Eiou(prediction, target)
TypeError: bbox_iou() got an unexpected keyword argument 'EIoU'
请问训练迭代一次epoch后遇到上面情况应该如何处理呀?
