vit for few-shot classification

Last update: Nov 30, 2022

Related tags

Deep Learning few-shot-vit

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

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Comments

timm version

hello, I met a question when run your code as follow? Traceback (most recent call last): File "train_classifier.py", line 296, in <module> main(config) File "train_classifier.py", line 133, in main lr_scheduler = CosineLRScheduler(optimizer, warmup_lr_init=float(config['optimizer_args']['warmup_lr']), t_initial=config['max_epoch'], cycle_decay=0.1, warmup_t=int(config['optimizer_args']['warmup'])) TypeError: __init__() got an unexpected keyword argument 'cycle_decay' I think it's the version of timm package is not right, and the requirement in your code just say that is the latest version. can your provide the version of timm package??

opened by JIAOJIAYUASD 2
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

vit for few-shot classification

Related tags

Overview

Few-Shot ViT

Requirements

Pretrained Checkpoints

Evaluate the Pretrained Checkpoints

Prepare data

Prapare pretrained checkpoints

Evaluation

TODO

You might also like...

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PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners for self-supervised ViT.

A simple program for training and testing vit

Implementing Vision Transformer (ViT) in PyTorch

Comments

timm version

The variant of visformer

Releases(SUN)

SUN(Jun 5, 2022)

Owner

Martin Dong

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