Comments (2)
KeyKy
commented on June 9, 2024
I concatdataset the in1k 10 times. The acc is growing. I only get 13% acc in 10+ epoch.
I can get 82.6% acc when finetune the whole Imagenet(not in1k).
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KeyKy
commented on June 9, 2024
ImageNet-1K is a subset of the full ImageNet dataset [11], which consists of
14,197,122 images, divided into 21,841 classes. ImageNet-1K
was created by selecting a subset of 1.2M images from ImageNet-21K, that belong to 1000 mutually
exclusive classes.
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Related Issues (20)
- Small naming error - masking generation HOT 1
- Is the visualization result normal? HOT 2
- Ask for segmentation finetune code
- Confusion in The Loss Function Implementation.
- param_groups_lrd for layer decay HOT 1
- Loss is considerably worse on custom data set with different mean and standard deviation HOT 2
- Error in loading pretrained weight for 'mae_vit_base_patch16' HOT 2
- About the gan-loss HOT 2
- patchify and unpatchify HOT 1
- I found both LLAMA and MAE used smaller beta2 in ADAMW optimizer during pre-training. Is that any intuition behind such setting? HOT 1
- How to obtain the reconstructed image for inference and masked
- model.fc_norm is not trained in linear probing
- visualization attention map.
- Could you provide the pretrained checkpoints of both encoder and decoder in MAE? HOT 2
- Is the training procedure result normal? Masked regions do not improve and appear to be random noise. HOT 2
- Two different checkpoints for each ViT type HOT 5
- Code: Compatible to any channels for function patchify and unpatchify HOT 2
- collab notebook error HOT 2
- How to obtain the complete reconstructed image?
- Can run interactive visualization demo with GPU?
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