ReID_baseline

Baseline model (with bottleneck) for person ReID (using softmax and triplet loss). This is PyTorch version, mxnet version has a better result and more SOTA methods.

We support

• multi-GPU training
• easy dataset preparation
• end-to-end training and evaluation

Get Started

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 0.4
   • torchvision
   • tensorflow (for tensorboard)
   • tensorboardX

4. Prepare dataset

   Create a directory to store reid datasets under this repo via

   cd reid_baseline
   mkdir data
   

   1. Download dataset to data/ from http://www.liangzheng.org/Project/project_reid.html
   2. Extract dataset and rename to market1501. The data structure would like:

   market1501/
       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 ~.torch/models/, you should set this path in config.py

Train

You can run

bash scripts/train_triplet_softmax.sh

in reid_baseline folder if you want to train with softmax and triplet loss. You can find others train scripts in scripts.

Results

network architecture ResNet50 -> global avg pooling -> BN(freeze beta) -> softmax (triplet)

config	Market1501	CUHK03
bs(32) size(384,128) softmax	92.2 (78.5)
bs(64) size(384,128) softmax	92.5 (79.6)
bs(32) size(256,128) softmax	92.0 (78.4)
bs(64) size(256,128) softmax	91.7 (78.3)
triplet	88.8%	74.8%
triplet + softmax	92.0%	78.1%