SOTA Re-identification Methods and Toolbox

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FastReID

FastReID is a research platform that implements state-of-the-art re-identification algorithms.

Quick Start

The designed architecture follows this guide PyTorch-Project-Template, you can check each folder's purpose by yourself.

cd to folder where you want to download this repo
Run git clone https://github.com/L1aoXingyu/fast-reid.git
Install dependencies:
- pytorch 1.0.0+
- torchvision
- yacs
Prepare dataset Create a directory to store reid datasets under projects, for example
```
cd fast-reid
mkdir datasets
```
1. Download dataset to datasets/ from baidu pan or google driver
2. Extract dataset. The dataset structure would like:
```
datasets
    Market-1501-v15.09.15
        bounding_box_test/
        bounding_box_train/
```
Prepare pretrained model. If you use origin ResNet, you do not need to do anything. But if you want to use ResNet_ibn, you need to download pretrain model in here. And then you can put it in ~/.cache/torch/checkpoints or anywhere you like.

Then you should set the pretrain model path in configs/Base-bagtricks.yml.

compile with cython to accelerate evalution

cd fastreid/evaluation/rank_cylib; make all

We provide a large set of baseline results and trained models available for download in the Fastreid Model Zoo.