vit for few-shot classification

Overview

Few-Shot ViT

Requirements

  • PyTorch (>= 1.9)
  • TorchVision
  • timm (latest)
  • einops
  • tqdm
  • numpy
  • scikit-learn
  • scipy
  • argparse
  • tensorboardx

Pretrained Checkpoints

Currently we provide SUN-M (Visformer) trained on miniImageNet (5-way 1-shot and 5-way 5-shot), see Google Drive for details.

More pretrained checkpoints coming soon.

Evaluate the Pretrained Checkpoints

Prepare data

For example, miniImageNet:

cd test_phase

Download miniImageNet dataset from miniImageNet (courtesy of Spyros Gidaris)

unzip the package to materials/mini-imagenet, then obtain materials/mini-imagenet with pickle files.

Prapare pretrained checkpoints

Download corresponding checkpoints from Google Drive and store the checkpoints in test_phase/ directory.

Evaluation

cd test_phase
python test_few_shot.py --config configs/test_1_shot.yaml --shot 1 --gpu 1 # for 1-shot
python test_few_shot.py --config configs/test_5_shot.yaml --shot 5 --gpu 1 # for 5-shot

For 1-shot, you can obtain: test epoch 1: acc=67.80 +- 0.45 (%)

For 5-shot, you can obtain: test epoch 1: acc=83.25 +- 0.28 (%)

Test accuracy may slightly vary with different pytorch/cuda versions or different hardwares

TODO

  • more checkpoints
  • training code
Issues
  • The variant of visformer

    The variant of visformer

    Hi Bowen

    Thanks for opensource the inference code. I am just curious which variant of the visformer achieves the best results in Table 5 on mini-ImageNet? Is it visformer_80_small?

    opened by RongKaiWeskerMA 1
Owner
Martin Dong
HIT student, major in Computer Science and Technology. CS.CV, object detection, segmentation, generation.
Martin Dong
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