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License: Apache License 2.0
A sample of relabel dataset has shape of [2,5,15,15]
Could you explain what each axis means?
I believe that [0, :] means prediction confidence and [1:,] means predicted class number.
Is there a reasoning script, can you view the results?
Hi
nice work, i enjoyed reading the article.
i noticed that you are using "plain" cutmix in the training.
have you considered doing "relabeld-cutmix", meaning pooling the targets from the relevant area only ?
it is definitely more complicated, but I think it can even count as a new interesting type of augmentation, that can be used
not only when learning from a teacher
Tal
Could you provide another source of relabeled-dataset? Dropbox really annoys me when it breaks.
Hi, I reproduced your paper with resnet 18 and 2A100 GPU, batch_size: 1024 + cutmix, but my result is not good (about 72.004%)
Epoch: [299][250/626] Time 1.088 (1.088) Speed 1882.005 (1882.005) LR 2.05E-08 Loss 2.3402 (2.3402) Prec@1 66.016 (66.016) Prec@5 86.719 (86.719)
Epoch: [299][500/626] Time 1.103 (1.096) Speed 1856.418 (1869.124) LR 2.30E-09 Loss 2.2720 (2.3061) Prec@1 69.775 (67.896) Prec@5 88.477 (87.598)
[Epoch 299] 780.568 sec/epoch remaining time: 0.000 hours
* Prec@1 72.004 Prec@5 90.296
Your report is 72.5 %
ResNet-18 | 71.7 | 72.5 (+0.8) [model_file]
Any suggestion to reproduce your result? Thanks
paragraph
def get_relabel(label_maps, batch_coords, num_batches):
target_relabel = roi_align(
input=label_maps,
boxes=torch.cat(
[torch.arange(num_batches).view(num_batches,
1).float().cuda(),
batch_coords.float() * label_maps.size(3) - 0.5], 1),
output_size=(1, 1))
target_relabel = torch.nn.functional.softmax(target_relabel.squeeze(), 1)
return target_relabel
line
batch_coords.float() * label_maps.size(3) - 0.5
batch_coords is given by percentage [x0,y0,x1,y1],
and it recovers to region [0,15].
why coords is substracted by 0.5?
https://www.dropbox.com/s/9sxigpec7fxq8wh/relabel_imagenet.tar?dl=0 여기 링크에서 데이터 다운로드 계속 실패인데..혹시 다른 방법 더 있을가여??
Could you release the code which is used to relabel imagenet ?
if i use our data,do not use ImageNet data,so,how do we get Relabel data?
Thank you very much for your meaningful research work!
For 4.3 Multi-Label Classification, I would like to ask where is the code for LabelPooling?
Oh no. I thought i could use the relabeled dataset to test my model, so i download imagenet val in 10GB.
But it is that the relabeled is for the training split which is 100GB.
Do you have relabeled val? I am doing the downloading.
BTW. Is ImageNet 2012? Does train and val overlap in samples?
Hi,
This is exciting work! Would you be able to please provide the code that you used to generate the relabel imagenet maps? I would like to apply this technique to my own dataset and the relabeling code would be super helpful. Even if it's not polished, it would be a great help :)
Thanks,
Thank you for the inspiring work! I have read the paper and the code, and would like to raise a question about the selection of the training hyper-parameters. Specifically, I found that you use custom setups for optimizers (e.g., SGD v.s. AdamP, learning rate, weight decay, etc.) in three configs (baseline, relabel, relabel+cutmix). I am wondering that how did you tune it? Are there any policies? Thanks!
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