README.md

TTAC on ImageNet

TTAC on ImageNet under common corruptions.

Requirements

• To install requirements:

  pip install -r requirements.txt
  

• To download dataset:

  We need to firstly download the validation set and the development kit (Task 1 & 2) of ImageNet-1k on here, and put them under data folder.

  The structure of the data folder should be like

  data
  |_ ILSVRC2012_devkit_t12.tar
  |_ ILSVRC2012_img_val.tar
  

• To create the corruption dataset

  python utils/create_corruption_dataset.py
  

  The issue Frost missing after pip install can be solved following here.

  Finally, the structure of the data folder should be like

  data
  |_ ILSVRC2012_devkit_t12.tar
  |_ ILSVRC2012_img_val.tar
  |_ val
      |_ n01440764
      |_ ...
  |_ corruption
      |_ brightness.pth
      |_ contrast.pth
      |_ ...
  |_ meta.bin
  

Pre-trained Models

Here, we use the pretrain model provided by torchvision.

Results

We mainly conduct our experiments under the sTTT (N-O) protocol, which is more realistic and challenging.

• run TTAC on ImageNet-C under the sTTT (N-O) protocol.

  bash scripts/run_ttac_no.sh
  

  The following results are yielded by the above script (classification errors) under the snow corruption:

  Method	ImageNet-C (Level 5)
  Test	82.22
  TTAC	44.56

• run TTAC on ImageNet-C under the N-O without queue protocol.

  In the sTTT protocol, we employ a sample queue (for all comparing methods), storing past samples, to aid model adaptation to enhance stability and improve accuracy. Obviously, it would bring more computing cost.

  Therefore, we provide the version of TTAC without queue which can be utilized in cases where efficiency is important.

  bash scripts/run_ttac_no_without_queue.sh
  

  The following results are yielded by the above script (classification errors) under the snow corruption:

  Method	ImageNet-C (Level 5)
  Test	82.22
  TTAC	46.64