Closed xiyiyia closed 2 years ago
Hi, thanks for your interest in our work. Your accuracy is close to random prediction (1/1000 for top-1 and 5/1000 for top-5), so I think it is likely that you are using the incorrect code or dataset. The names of folders in ImageNet val should be wordnet IDs like n01440764
. The class id predicted by our networks should be consistent with the argsort
results of these wordnet IDs. I think you can follow the instructions here to prepare the ImageNet dataset.
Hi, thanks for your interest in our work. Your accuracy is close to random prediction (1/1000 for top-1 and 5/1000 for top-5), so I think it is likely that you are using the incorrect code or dataset. The names of folders in ImageNet val should be wordnet IDs like
n01440764
. The class id predicted by our networks should be consistent with theargsort
results of these wordnet IDs. I think you can follow the instructions here to prepare the ImageNet dataset.
Thanks a lot!
Tried arch: deit_small, deit_256 Dataset: Imagenet-1k-val File structure:
When I ran
python3 infer.py --data-path /home/ubuntu/datasets/ILSVRC2012_val/ --arch deit_small --model-path /home/ubuntu/models/dynamic-vit_384_r0.7.pth --base_rate 0.7
, the result isAcc@1 0.080 Acc@5 0.582
.The filenames ( image label ) are decided by
ILSVRC2012_validation_ground_truth.txt
in the development kit. Is this problem due to the wrong file name, which causes the model to predict a different label than the real one? Should I modify the filename to WNID? But the Val dataset has no WNID, how could I confirm that?Thanks