Code for pet biometrics challenge



  • Linux or macOS with python ≥ 3.6

  • PyTorch ≥ 1.6

  • yacs

  • Cython (optional to compile evaluation code)

  • tensorboard (needed for visualization): pip install tensorboard

  • gdown (for automatically downloading pre-train model)

  • sklearn

  • termcolor

  • tabulate

  • faiss pip install faiss-gpu

  • for conda

    conda create -n fastreid python=3.8
    conda activate fastreid
    conda install pytorch==1.7.1 torchvision tensorboard -c pytorch
    pip install -r docs/requirements.txt

We use GPU 3090 for training and testing. The cuda version is 11.1, torch version is 1.7.1, the python version is 3.8.8.

Prepare Dataset

Download the competition datasets pet_biometric_challenge_2022, and then unzip them under the directory like

├── pet_biometric_challenge_2022
│   └── train
│   └── validation
│   └── test

Prepare Pre-trained Models

We have trained 8 models, and you can download the pre-trained model form this link: 链接: 提取码:bauk. Then you should save it under the path of logs. The file tree should be like as:

└── resnet101
    └── config.yaml
    └── model_final.pth


You can get the final submit.csv by runing:


It will generate submit.csv in the root dir, which is the final ensemble result. The test process takes approximately 20mins.



We train our model through three stage. Stage1 train the original dataset with 224 resolution by different losses , backbone and batchsize. Stage2 finetune the trainset with 384 resolution which is inspired by kaggle-landmark-2021-1st-place. Stage3 finetune the model with trainset and validation set which is assigned with pseudo labels. The training process takes approximately 64 hours.



View Github