1st and Future - Player Contact Detection

Below you can find an outline of how to reproduce my 1st place solution for the 1st and Future - Player Contact Detection.

1.INSTALLATION

• Ubuntu 18.04.5 LTS
• CUDA 11.2
• Python 3.7.5
• Training PC: 1x RTX3090 (or any GPU with at least 24Gb VRAM), 32GB RAM, at least 5TB of disk space.
• python packages are detailed separately in requirements.txt

$ conda create -n envs python=3.7.5
$ conda activate envs
$ pip install -r requirements.txt
$ pip install torch==1.10.1+cu113 torchvision==0.11.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
$ pip install -U openmim
$ mim install mmcv-full

2.DATA

• Download dataset and extract to data/ folder.
• Download pretrained mmaction2 weight wget -P pretrained/ "https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ircsn_ig65m_pretrained_r50_32x2x1_58e_kinetics400_rgb_20210617-86d33018.pth"

3. PREPROCESSING

• Create folds and cache helmet boxes and tracking data to dictionary.

$ python create_folds.py
$ python cache_metadata.py

• Train xgb preprocessing to filter easy negative sample.

$ cd tree/
$ python pre_g_xgb.py
$ python pre_p_xgb.py

• Generate cache for CNN model and save to disk.

$ cd cnn/
$ python create_cache11_G.py
$ python create_cache15_G.py
$ python create_cache15.py

• The project folder now looks like
  ├── data
  │ ├── train
  │ ├── test
  │ ├── train_player_tracking.csv
  │ ├── train_folds.csv
  │ ├── ...
  ├── tree
  │ ├── pp_g_xgb.py
  │ ├── pp_p_xgb.py
  │ ├── pre_g_xgb
  │ ├── ...
  ├── cnn
  │ ├── configs
  │ │ ├── r50ir_csn_c11_m1_d2_G_all.py
  │ │ ├── r50ir_csn_c15_m1_d2_all.py
  │ │ ├── ...
  │ ├── models
  │ │ ├── model_csn1.py
  │ │ ├── resnet3d_csn.py
  │ ├── dataset
  │ │ ├── dataset_3d_3ch_v2.py
  │ ├── cache
  │ │ ├── cache11_G
  │ │ ├── cache15_G
  │ │ ├── cache15
  │ ├── pretrained/
  │ ├── train.py
  │ ├── inference.py
  │ ├── create_cache11_G.py
  │ ├── create_cache15_G.py
  │ ├── create_cache15.py
  │ ├── train.sh
  │ ├── ...
  ├── cache_metadata.py
  ├── create_folds.py
  ├── readme.md
  ├── requirements.txt

4.TRAINING

• Preprocessing step must be run before this step.
• To train and validate all the models, run the following command inside the cnn/ folder.

$ ./train.sh

• Folds 0,1,2,3,4 are used for validation and train a xgb post processing model.
• Folds 5,6,7,8,9 are trained with all data and used in submission. PP model used fold 5,6,7,8; PG model used fold 6,7,8 from 2 configs (6 checkpoints).

• Train xgb post processing.

$ cd tree/
$ python pp_g_xgb.py
$ python pp_p_xgb.py

5.INFERENCE

• Since this is a code competition, the inference code is available in kaggle notebook.
• https://www.kaggle.com/code/nvnnghia/nfl3-ensemble-w-pp-sub?scriptVersionId=121109062