Image Classification in PyTorch

This repository contains the implementation of image classification models in PyTorch.

Prerequisites

• python >= 3.6
• torch >= 1.8.1
• torchvision >= 0.9.1

Usage

1. Clone the repository and install the required dependencies with the following command:

$ git clone https://github.com/woohyun-jeon/pytorch-classification.git
$ cd pytorch-classification
$ pip install -r requirements.txt

2. Download ImageNet into datasets directory

The directory structure should be as follows:

  datasets/
    ILSVRC/      
      Annotations/
        CLS-LOC/
            train/
                n01440764/
                    n01440764_10040.JPEG
                    ...
                ...
            val/
                n01440764/
                    n01440764_0000001.JPEG
                ...
      Data/
        CLS-LOC/
            train/
                n01440764/
                    n01440764_10040.xml
                    ...
                ...
            val/
                n01440764/
                    n01440764_0000001.xml
                ...
            test/
                *
                ...
      ImageSets/
        CLS-LOC/
            test.txt
            train_cls.txt
            train_loc.txt
            val.txt      

3. Run python train.py for training

Supported Models

• Inception v1
• VGGNet
• ResNet
• Inception v2,3
• Pre-Activation ResNet
• ResNext
• DenseNet
• Inception v4
• SqueezeNet
• Wide Residual Networks
• Xception
• Dual Path Networks
• MobileNet v1
• MobileNet v2
• Residual Attention Network
• MnasNet
• ShuffleNet v1
• ShuffleNet v2
• SE-ResNet
• CBAM-ResNet
• EfficientNet
• Vision Transformer
• Swin Transformer