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Destruction and Construction Learning for Fine-grained Image Recognition

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在cuda11 torch1.8下运行会报runtime:error forward static method错,在torch 1.2 rtx 2070s下运行是正常的,请给你指导和帮忙,谢谢

In the paper, on CUB they mentioned 87.8% accuracy on CUB, but I can only get 86.9% using this repository.

我遇到了这样的错误,有大佬知道这是为啥吗,感谢感谢 TypeError: img should be PIL Image. Got

When I train the model in 448 resolutions, it works fine. But when I set `--size 256 --crop 224`, Error occurs. How can I fix this error?

i only achieved the 90.4% acc on AIR dataset, have anyone achieved 93%?

Hi, I recently did some research on fine-grained classification, and I find that there are only train.txt and test.txt in your directory 'dataset'. Is the 'val.txt' in the code actually...

Specifically, where is the loc_loss in code? where is the adversarial loss? I can't find the regression between the predicted coordinates and original coordinates, where is that? Many thanks!

Namespace(auto_resume=False, backbone='resnet50', base_lr=0.0008, check_point=5000, cls_2=False, cls_lr_ratio=10.0, cls_mul=False, crop_resolution=448, dataset='CUB', decay_step=60, discribe='', epoch=360, resize_resolution=512, resume=None, save_point=5000, start_epoch=0, swap_num=[7, 7], train_batch=16, train_num_workers=16, val_batch=512, val_num_workers=32) **Traceback** (most recent call last): File "e:/graduation_project/object_detection-master/DCL-master/train.py", line...

when I run the test.py, I got the error. How can I solve it ? ---- Namespace(acc_report=True, auto_resume=True, backbone='resnet50', batch_size=4, crop_resolution=448, dataset='SHIP', num_workers=0, resize_resolution=512, resume=None, save_suffix=None, swap_num=[7, 7], version='test') _42315_SHIP...