The code of XJTU_MM for SoccerNet2024 GameState

The method is shown in the report of Optimizing Jersey Number Recognition for Effective Player Tracking in the Game State Reconstruction

The leaderboard of SoccerNet2024 GameState

git clone https://github.com/Xv-M-S/GameState-MM.git

Create and activate a new environment

conda create -n tracklab pip python=3.10 pytorch==1.13.1 torchvision==0.14.1 pytorch-cuda=11.7 -c pytorch -c nvidia -y
conda activate tracklab

Install the dependencies for tracklab

cd tracklab/plugins/track
pip install -e . -i https://pypi.org/simple  # note：使用pip的默认源安装

cd tracklab
pip install -e . -i https://pypi.org/simple  # note 使用pip默认源安装
mim install mmcv==2.0.1

Install the dependencies for sn-gamestate

cd sn-gamestate/plugins/calibration
pip install -e . -i https://pypi.org/simple  # note 使用pip默认源安装

cd sn-gamestate
pip install -e . -i https://pypi.org/simple  # note 使用pip默认源安装

If you want to download the dataset manually, you can run the following snippet after installing the soccernet package (pip install SoccerNet) :

from SoccerNet.Downloader import SoccerNetDownloader
mySoccerNetDownloader = SoccerNetDownloader(LocalDirectory="data/SoccerNetGS")
mySoccerNetDownloader.downloadDataTask(task="gamestate-2024",
                                       split=["train", "valid", "test", "challenge"])

After running this code, please unzip the folders, so that the data looks like :

data/
   SoccerNetGS/
      train/
      valid/
      test/
      challenge/

You can unzip them with the following command line :

cd data/SoccerNetGS
unzip gamestate-2024/train.zip -d train
unzip gamestate-2024/valid.zip -d valid
unzip gamestate-2024/test.zip -d test
unzip gamestate-2024/challenge.zip -d challenge
cd ../..

External dependencies

• DATA: You will need to set up some variables before running the code in soccernet.yaml(sn_gamestate/configs/soccernet.yaml)
  • data_dir: the directory where you will store the different datasets (must be an absolute path !). If you opted for the automatic download option, data_dir should already point to the correct location.
• MODEL: Download the pretrained model weights here and put the "pretrained_models" directory under the main project directory (i.e. "/path/to/tracklab/pretrained_models/reid").
• YoloModel: Dowlaod the pretrained YOLOv8 model weights here and put the "yolov8x6.pt" file under the main project directory (i.e. "/path/to/tracklab/pretrained_models/yolo").

cd sn-gamestate
python -m tracklab.main -cn soccernet