ReID_baseline

A strong baseline (state-of-the-art) for person re-identification.

We support

• easy dataset preparation
• end-to-end training and evaluation
• multi-GPU distributed training
• fast training speed with fp16
• support both image and video reid
• multi-dataset training
• cross-dataset evaluation
• high modular management
• state-of-the-art performance with simple model
• high efficient backbone
• advanced training techniques
• various loss functions
• visualization tools

Get Started

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/reid_baseline.git

3. Install dependencies:

   • pytorch 1.0.0+
   • torchvision
   • fastai
   • yacs

4. Prepare dataset

   Create a directory to store reid datasets under this repo via

   cd reid_baseline
   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 don't have

   from torchvision import models
   models.resnet50(pretrained=True)
   

   Then it will automatically download model in ~/.cache/torch/checkpoints/, you should set this path in config/defaults.py for all training or set in every single training config file in configs/.

Train

Most of the configuration files that we provide, you can run this command for training market1501

bash scripts/train_market.sh

Or you can just run code below to modify your cfg parameters

python3 tools/train.py -cfg='configs/softmax.yml' INPUT.SIZE_TRAIN '(256, 128)' INPUT.SIZE_TEST '(256, 128)'

Test

You can test your model's performance directly by running this command

python3 tools/test.py --config_file='configs/softmax.yml' TEST.WEIGHT '/save/trained_model/path'

Results

cfg	market1501	dukemtmc
softmax_triplet, size=(256, 128), batch_size=64(16 id x 4 imgs)	93.9 (85.9)	training