ReID_baseline

The results in CUHK03 is wrong becuase I use the same evaluation protocol with market1501 and duke. I will fix this later. You can use the right evaluation protocol to get accurate result.

Baseline model (with bottleneck) for person ReID (using softmax and triplet loss).

We support

• easy dataset preparation
• end-to-end training and evaluation
• high modular management

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
   • torchvision
   • ignite
   • yacs

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:

   data
       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/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

python3 tools/train.py --config_file='configs/softmax.yml' DATASETS.NAMES "('market1501')"

You can also modify your cfg parameters as follow

python3 tools/train.py --config_file='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

network architecture

cfg	market1501	dukemtmc
softmax, size=(384, 128), batch_size=64	92.5 (79.4)	84.6 (68.1)
softmax, size=(256, 128), batch_size=64	92.0 (80.4)	84.1(68.4)
softmax_triplet, size=(384, 128), batch_size=128(32 id x 4 imgs)	93.2 (82.5)	86.4 (73.1)
softmax_triplet, size=(384, 128), batch_size=64(16 id x 4 imgs)	93.8 (83.2)	86.2 (72.9)
softmax_triplet, size=(256, 128), batch_size=64(16 id x 4 imgs)	93.8 (85.3)	86.0 (74.0)