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

config	Market1501
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)
bs(128) size(256,128) softmax	91.2 (77.4)
triplet(p=32,k=4) size(256,128)	88.3 (73.8)
triplet(p=16,k=4)+softmax size(384,128)	93.1 (82.0)
triplet(p=24,k=4)+softmax size(384,128)	91.7 (79.0)