# pytorch-classification

**Repository Path**: cmy_program/pytorch-classification

## Basic Information

- **Project Name**: pytorch-classification
- **Description**: Classification with PyTorch.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-12-10
- **Last Updated**: 2021-12-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# pytorch-classification
Classification on CIFAR-10/100 and ImageNet with PyTorch.

## Features
* Unified interface for different network architectures
* Multi-GPU support
* Training progress bar with rich info
* Training log and training curve visualization code (see `./utils/logger.py`)

## Install
* Install [PyTorch](http://pytorch.org/)
* Clone recursively
  ```
  git clone --recursive https://github.com/bearpaw/pytorch-classification.git
  ```

## Training
Please see the [Training recipes](TRAINING.md) for how to train the models.

## Results

### CIFAR
Top1 error rate on the CIFAR-10/100 benchmarks are reported. You may get different results when training your models with different random seed.
Note that the number of parameters are computed on the CIFAR-10 dataset.

| Model                     | Params (M)         |  CIFAR-10 (%)      | CIFAR-100 (%)      |
| -------------------       | ------------------ | ------------------ | ------------------ |
| alexnet                   | 2.47               | 22.78              | 56.13              |
| vgg19_bn                  | 20.04              | 6.66               | 28.05              |
| ResNet-110                | 1.70               | 6.11               | 28.86              |
| PreResNet-110             | 1.70               | 4.94               | 23.65              |
| WRN-28-10 (drop 0.3)      | 36.48              | 3.79               | 18.14              |
| ResNeXt-29, 8x64          | 34.43              | 3.69               | 17.38              |
| ResNeXt-29, 16x64         | 68.16              | 3.53               | 17.30              |
| DenseNet-BC (L=100, k=12) | 0.77               | 4.54               | 22.88              |
| DenseNet-BC (L=190, k=40) | 25.62              | 3.32               | 17.17              |


![cifar](utils/images/cifar.png)

### ImageNet
Single-crop (224x224) validation error rate is reported. 


| Model                | Params (M)         |  Top-1 Error (%)   | Top-5 Error  (%)   |
| -------------------  | ------------------ | ------------------ | ------------------ |
| ResNet-18            | 11.69              |  30.09             | 10.78              |
| ResNeXt-50 (32x4d)   | 25.03              |  22.6              | 6.29               |

![Validation curve](utils/images/imagenet.png)

## Pretrained models
Our trained models and training logs are downloadable at [OneDrive](https://mycuhk-my.sharepoint.com/personal/1155056070_link_cuhk_edu_hk/_layouts/15/guestaccess.aspx?folderid=0a380d1fece1443f0a2831b761df31905&authkey=Ac5yBC-FSE4oUJZ2Lsx7I5c).

## Supported Architectures

### CIFAR-10 / CIFAR-100
Since the size of images in CIFAR dataset is `32x32`, popular network structures for ImageNet need some modifications to adapt this input size. The modified models is in the package `models.cifar`:
- [x] [AlexNet](https://arxiv.org/abs/1404.5997)
- [x] [VGG](https://arxiv.org/abs/1409.1556) (Imported from [pytorch-cifar](https://github.com/kuangliu/pytorch-cifar))
- [x] [ResNet](https://arxiv.org/abs/1512.03385)
- [x] [Pre-act-ResNet](https://arxiv.org/abs/1603.05027)
- [x] [ResNeXt](https://arxiv.org/abs/1611.05431) (Imported from [ResNeXt.pytorch](https://github.com/prlz77/ResNeXt.pytorch))
- [x] [Wide Residual Networks](http://arxiv.org/abs/1605.07146) (Imported from [WideResNet-pytorch](https://github.com/xternalz/WideResNet-pytorch))
- [x] [DenseNet](https://arxiv.org/abs/1608.06993)

### ImageNet
- [x] All models in `torchvision.models` (alexnet, vgg, resnet, densenet, inception_v3, squeezenet)
- [x] [ResNeXt](https://arxiv.org/abs/1611.05431)
- [ ] [Wide Residual Networks](http://arxiv.org/abs/1605.07146)


## Contribute
Feel free to create a pull request if you find any bugs or you want to contribute (e.g., more datasets and more network structures).