# classification

**Repository Path**: Tsingwaa/classification

## Basic Information

- **Project Name**: classification
- **Description**: A template of deep learning experiment on classification
- **Primary Language**: Python
- **License**: GPL-3.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-09-16
- **Last Updated**: 2022-08-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: classification, PyTorch

## README

# General Framework for Image Classification

It is a general framework for image classification. Specifically, our research field is imbalanced image classification, therefore, our framework integrates various imbalanced methods.

## Introduce

* **base**: all parent wrap module for trainer, dataset, network, etc.
  * **base_trainer**: BaseTrainer including all basic functionality, e.g. set configs, initilize modules
  * **base_sampler**: TODO
  * **base_dataset**: TODO
  * **base_module**: TODO
  * **base_loss**: TODO
* **data_loader**: all module related to loading data
  * **data**: images lists of various dataset
  * **dataset**: various Dataset class inherited from torch.utils.data.dataset.Dataset
  * **sampler**: various Sampler class inherited from torch.utils.data.sampler.Sampler
  * **transform**: various transforms for data augmentation
* **model**: all modules related to model
  * **network**: general backbones, e.g. ResNet, ResNeXt
  * **module**: special modules, e.g. adversarial attack, mixup/cutmix.
  * **loss**: various Loss class inherited from torch.nn.Module
* **scripts**: scripts related to specific experiment
  * **various experiment scripts**: one script folder for each experiment
    * **configs**: store all .yaml run configs by dataset
    * ***.py**: the python script including Trainer inherited from the BaseTrainer
    * ***.sh**: the shell script corresponding to the python script
* **utils**: various functional utility
  * **metrics**: modules about measure the performance
  * **compute_mean_std**: get mean and std for various dataset to normalize
  * **other utility**: not commonly used

## How to use

1. clone the repository
2. create a new copy of "scripts/baseline" as your experiment folder
3. if special module is required, add the module to the corresponding parent folder (Note: add the new module to Registry dict, i.e. decorate register_module)
4. rewrite the *.py script inherited from BaseTrainer imitating "scripts/baseline/train.py"
5. configure the experiment .yaml
6. rewrite the *.sh script imitating "scripts/baseline/train.sh"
7. run the *.sh script

## FAQ

* What is Registry?
  * Kind of dictionary to get class by the key, i.e. class name.
* Single-GPU or Distributed training?
  * Referred to the *.sh script, if single-gpu, manually set the local_rank to -1, if multi-gpu, let the local_rank automatically set by torch.distributed.launch.

## Note

This repo is now not open to the public. But I will systematically optimize and open this framework when my graduation research is finished.

## Acknowledge

Special thanks to Xiaoping Wu and Shaobo Guo!