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.

1. cd to folder where you want to download this repo

2. Run git clone https://github.com/L1aoXingyu/fast-reid.git

3. Install dependencies:

   • pytorch 1.0.0+
   • torchvision
   • tensorboard
   • yacs

4. Prepare dataset Create a directory to store reid datasets under projects, for example

   cd fast-reid/projects/StrongBaseline
   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/
   

5. 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/baseline_market1501.yml.

6. compile with cython to accelerate evalution

   cd fastreid/evaluation/rank_cylib; make all
   

Model Zoo and Baselines

Market1501 dataset

Method	Pretrained	Rank@1	mAP	mINP
BagTricks	ImageNet	93.6%	85.1%	58.1%
BagTricks + Ibn-a	ImageNet	94.8%	87.3%	63.5%
AGW	ImageNet	94.9%	87.4%	63.1%

DukeMTMC dataset

Method	Pretrained	Rank@1	mAP	mINP
BagTricks	ImageNet	86.1%	75.9%	38.7%
BagTricks + Ibn-a	ImageNet	89.0%	78.8%	43.6%
AGW	ImageNet	88.9%	79.1%	43.2%

MSMT17 dataset

Method	Pretrained	Rank@1	mAP	mINP
BagTricks	ImageNet	70.4%	47.5%	9.6%
BagTricks + Ibn-a	ImageNet	76.9%	55.0%	13.5%
AGW	ImageNet	75.6%	52.6%	11.9%