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SoccerNetv2-ReplayGrounding-NetVLAD-More-Negative

README.md

SoccerNet-v2 - Replay Grounding - NetVLAD

This repository contains the code to reproduce the replay grounding result of the paper: "SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos" with NetVLAD baslines:

NetVLAD method is utilized within Siamese framework. Additionally we mine the negative samples for each replay (5 negative for the training ) The SoccerNet-v2 paper can be found here: SoccerNet-v2 paper, and the NetVLAD paper.

@InProceedings{Deliege2020SoccerNetv2,
  author = { Deliège, Adrien and Cioppa, Anthony and Giancola, Silvio and Seikavandi, Meisam J. and Dueholm, Jacob V. and Nasrollahi, Kamal and Ghanem, Bernard and Moeslund, Thomas B. and Van Droogenbroeck, Marc},
  title = {SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos},
  booktitle = {CoRR},
  month = {Nov},
  year = {2020}
}
@InProceedings{Giancola_2018_CVPR_Workshops,
  author = {Giancola, Silvio and Amine, Mohieddine and Dghaily, Tarek and Ghanem, Bernard},
  title = {SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {June},
  year = {2018}
}

The task consists in retrieving the timestamp of the action shown in a given replay shot within the whole game.

Getting Started

The following instructions will help you install the required libraries and the dataset to run the code. The code runs in python 3 and was tested in a conda environment. Pytorch is used as deep learning library.

Create environment

To create and setup the conda environment, simply follow the following steps:

conda create -n CALF-pytorch python=3.8
conda activate CALF-pytorch
conda install pytorch=1.6 torchvision=0.7 cudatoolkit=10.1 -c pytorch
pip install SoccerNet

Run training and testing

The code for training and testing the network is located inside the src folder under the name main.py.

To train the network, simply run the following command from the root folder:

python src/main.py --SoccerNet_path=/path/to/SoccerNet/ \
--features=ResNET_TF2_PCA512.npy \
--num_features=512 \
--model_name=CALF_v2 \
--batch_size 32 \
--evaluation_frequency 20 \
--chunk_size 120 \
--receptive_field 40\
--loop 5 \
--unsimilar_action 0.3 \
--hard_negative_weight 0.4 \
--pooling "NetVLAD"

You can save the results in json files for each game by adding this arguments:

 --detection_path '/outputs/' \
 --save_results True

The weights of the network will be saved in the models/model_name/ folder alongside a log file tracing the training parameters and the evolution of the performances. The predictions for the challenge submission on EvalAI (testset split) will be stored in the outputs folder. To submit your results, simply zip the league folders inside a single zip file.

Note that if you did not download the SoccerNet features beforehand, the code will start by downloading them, which might take a bit of time.

To evaluate the results run the model from the main.py file. This option will re-load the weights of your model and predict the results from the features, and save them in json format in the outputs folder:

python src/main.py --SoccerNet_path=/path/to/SoccerNet/ \
--features=ResNET_TF2_PCA512.npy \
--num_features=512 \
--model_name=NetVLAD_more_negative \
--test_only \
 --detection_path '/outputs/' \
 --save_results True

If you wish to use our pre-trained weights, they are located in models/NetVLAD_more_negative_trained/. You can reproduce our results by running:

python src/main.py --SoccerNet_path=/path/to/SoccerNet/ \
--features=ResNET_TF2_PCA512.npy \
--num_features=512 \
--model_name=NetVLAD_more_negative_trained \
--test_only \
 --detection_path '/outputs/' \
 --save_results True

For producing the results of the challenge, simply use the --challenge parameter. Of course, the performance won't be computed localy since you don't have access to the labels. Upload the predictions saved in the outputs folder on EvalAI to get your challenge performance.

python src/main.py --SoccerNet_path=/path/to/SoccerNet/ \
--features=ResNET_TF2_PCA512.npy \
--num_features=512 \
--challenge
--model_name=NetVLAD_more_negative_trained \
--test_only
--detection_path '/outputs/' \
--save_results True