FastReID is a research platform that implements state-of-the-art re-identification algorithms.
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
- Download dataset to
datasets/
from baidu pan or google driver - Extract dataset. The dataset structure would like:
datasets Market-1501-v15.09.15 bounding_box_test/ bounding_box_train/
- Download dataset to
-
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.