Basketball Detector Trainer

This repository contains all the necessary tools to train the :basketball: BasketballDetector model.

Table Of Contents

1. Description
2. Using the PaddleSeg toolbox
3. Environment setup
4. Model training
5. Model evaluation
6. Results
   1. OHEM Cross-Entropy loss function evaluation results
   2. Weighted Cross-Entropy loss function evaluation results
   3. Cross-test set results
   4. Rancrop model results
7. Credits

Description

This project uses the PaddleSeg toolkit to train a modified PPLiteSeg real-time semantic segmentation model. The configuration files used during training can be found here. In the following sections, you will find detailed instructions on how to set up a working environment and how to train a model.

Using the PaddleSeg toolbox

The segmentation model has been trained using a customized version of the sample configuration file for the PPLiteSeg model applied to the Cityscapes dataset found on the PaddleSeg repository.

Environment setup

Before being able to train the model, you must install Paddle and PaddleSeg. You can use one of the provided conda environments by running the following command:

conda create --name myenv-pp --file pp-[cpu|gpu].yml

It is recommended to have a CUDA enabled GPU in order to take advantage of GPU acceleration.

In case you're using the GPU version, don't forget to set up the required environment variables as well:

export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH

You can automate the process of adding the environment variables to execute automatically each time you activate your conda environment by running the following commands:

mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

The currently available Paddle PyPi GPU version release requires the CUDA Runtime API version 10.2 to be installed in order to run correctly. This dependency is therefore listed in the provided conda environment. If you want to use a different CUDA version, refer to the official documentation.

Also, to avoid unexpected errors, the PaddleSeg package should be built from source using the provided repository, while being in the myenv-pp environment:

cd PaddleSeg
pip install -v -e .

Model training

To train the BasketballDetector segmentation model, run:

python PaddleSeg/tools/train.py \
--configs configs/pp_liteseg_base_stdc1_ohem_10000_1024x512.yml \
--do_eval \
--use_vdl \
--save_interval 2500 \
--keep_checkpoint_max 20 \
--save_dir output

The trained models will then be available in the output/ directory. More information on what these options do and on how to visualize the training process can be found here.

Model evaluation

To evaluate the obtained model, run:

python PaddleSeg/tools/val.py \
--configs configs/pp_liteseg_base_stdc1_ohem_10000_1024x512.yml \
--model_path output/best_model/model.pdparams

For additional options refer to the official documentation.

Results

Various models have been trained using two different loss functions, in particular the OHEM Cross-Entropy loss function and the weighted Cross-Entropy loss function. All the used configurations with different parameters can be found here.

In the following tables you can find the summarized results.

OHEM Cross-Entropy loss function evaluation results

min_kept parameter value	Ball class IoU	Ball class Precision	Ball class Recall	Kappa	Links
40 x 40 x batch_size = 6400	0,6590	0,8527	0,7437	0,7944	config
50 x 50 x batch_size = 10000	0,6658	0,8770	0,7344	0,7993	config
60 x 60 x batch_size = 14400	0,6601	0,8895	0,7191	0,7952	config
70 x 70 x batch_size = 20000	0,6542	0,9090	0,7000	0,7979	config

Weighted Cross-Entropy loss function evaluation results

Background class weight	Ball class IoU	Ball class precision	Ball class Recall	Kappa	Links
0,001	0,2656	0,2700	0,9422	0,4195	config
0,005	0,4703	0,4927	0,9117	0,6396	config
0,01	0,5394	0,5729	0,9020	0,7007	config

The test set results were consistently better than the validation set results used during the evaluation phase, even after trying multiple dataset splits, possibly indicating an over-fitting problem due to the limited dataset size.

Therefore, a cross-test set has been evaluated as well, using images from a basketball field not seen during training, but still sufficiently similar (similar camera, similar camera angles). The complete report on this issue is available in the form of my master thesis here.

Cross-test set results

Model name	Ball class IoU	Ball class Precision	Ball class Recall	Kappa	Links
PP-LiteSeg-OHEM	0,4555	0,6784	0,5810	0,6258	config
PP-LiteSeg-WCE	0,3215	0,3639	0,7339	0,4863	config

Rancrop model results

The PPLiteSegRandomCrops model was validated using these configurations and these configurations, with the model described in this configuration used as the base model.

Various variants of the PPLiteSegRandomCrops model were tested, all available in this directory. In particular, different padding and aggregation methods were used. All the obtained results were worse than the chosen base PPLiteSeg model.

Credits

This project uses the PaddleSeg toolbox. All credits go to its authors. This project uses pdoc to generate its documentation. All credits go to its authors. The implemented PPLiteSegRandomCrops model takes inspiration from the paper Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup. All credits go to its authors.