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https://github.com/sailist/image-classification

Reimplementation of papers on image classification
https://github.com/sailist/image-classification

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Reimplementation of papers on image classification

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# 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](./results/ssl-knn.csv) for knn results.

    - [x] SimCLR V2, `python scripts/ssl/simclr.py --dataset=cifar100`

    - [x] 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](./results/semi-supervised-learning.csv) for error rates.

    - [x] 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:



```bash

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/](./datasets/datas/init.py), 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](./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](./datasets/ssldataset.py):



```python

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:



```python

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](./track_ssl/simclr.py)

/[moco](./track_ssl/moco.py), 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](https://github.com/lumo-tech/lumo).