image-classification

Reimplemented papers of supervised/semi-supervised/self-supervised learning

Reimplemented methods

(done flag means the results are validated)

• Self-supervised-learning , see ssl-knn.csv for knn results.

  • SimCLR V2, python scripts/ssl/simclr.py --dataset=cifar100
  • MoCo V2 python scrtips/ssl/moco.py --dataset=cifar100
  • SupContrast python scripts/ssl/supcontrast.py --dataset=cifar100

• Semi-supvised Learning, see semi-supervised-learning.csv for error rates.

  • CHMatch python scripts/semi/chmatch.py --dataset=cifar100
  • FlexMatch python scripts/semi/flexmatch.py --dataset=cifar100
  • FixMatch python scripts/semi/fixmatch.py --dataset=cifar100
  • CoMatch python scripts/semi/comatch.py --dataset=cifar100
  • MixMatch python scripts/semi/mixmatch.py --dataset=cifar100

• Supvised-learning.

Please refer to the issues corresponding to the meta_info column in the csv files to see the detailed hyperparameters.

How to reproduce

Run python scripts/semi/chmatch.py --dataset=cifar100 to see the commands to be executed, then use pipe to run the output with bash, like:

python scripts/semi/chmatch.py --dataset=cifar100 | bash

The scripts will take the GPU environment into account and assign device for each run.

Add your method

You may need to prepare: dataset (most of them are ready), data augmentation methods (most cases are ready), model (most of them are ready), and training code (core logic part).

Dataset

The method for reading all basic data sources is defined in datasets/datas/, which provides (xs, ys) sample pairs for most data. If the format you need is not in this directory, you need to implement it yourself.

Transform

Common augmentation methods are defined in augmentations/strategies.py. If what you need is not available, you can add one in the same format.

To use these augmentation strategies, taking self-supervised learning as an example, two methods are defined in，在dataset/ssldataset.py:

def get_train_dataset(dataset_name, method='basic', split='train'): ...


def get_test_dataset(dataset_name): ...

You can change the augmentation method you need in the method:

if method == 'essl':
    ds.add_output('xs', 'xsu', rotate(mean, std, size=img_size // 2, v=0))
    ds.add_output('xs', 'xsd', rotate(mean, std, size=img_size // 2, v=180))
    ds.add_output('xs', 'xsl', rotate(mean, std, size=img_size // 2, v=90))
    ds.add_output('xs', 'xsr', rotate(mean, std, size=img_size // 2, v=-90))
elif method == 'you method':
    ...
else:
    (ds
     .add_output('xs', 'xs', none(mean, std, size=img_size))
     .add_output('xs', 'sxs0', simclr(mean, std, size=img_size))
     .add_output('xs', 'sxs1', simclr(mean, std, size=img_size))
     .add_output('ys', 'ys')
     )

模型 & 训练

Taking self-supervised learning as an example, you can copy code such as simclr /moco, and implement your own code based on it. In most cases, the test code of the same track does not need to be rewritten.

The method is determined by Params.method, and the module is determined by the part of your training file without the suffix (os.path.basename()).

Acknowledge

This project is built on lumo.