README.md

Team members

Anvar Iskhakov
email: [email protected]

Albert Khazipov
email: [email protected]

Dmitrii Naumov
email: [email protected]

Prerequisites

Dependencies

Install all required packages with

pip install -r requirements.txt

Datasets

Original dataset

Download dataset from https://www.kaggle.com/datasets/risangbaskoro/wlasl-processed

And unzip in folder data/raw/dataset

Custom dataset

If you want to create your own dataset you can record videos for your own classes and put them in data/raw/custom_video_dataset. Video of each class should be located in the folder of it's class name. E.g. videos for class plane should be stored in data/raw/custom_video_dataset/plane

Prepare Data

To prepare wlasl dataset for further pre-processing enter following command for the repository root:

python src/data/dataset_preprocessing.py 

To prepare custom dataset for further pre-processing enter following command for the repository root:

python src/data/custom_dataset_preprocessing.py 

To pre-process merged dataset to further training enter following command for the repository root:

python src/data/video_keypoints_extractor.py 

Train model

To train any model on the preprocessed dataset enter following command for the repository root (example for a simple lstm model):

python src/models/simple_lstm/train_model.py

Inference

To use the final trained model on your own videos enter following command for the repository root (example for a simple lstm model):

python src/models/simple_lstm/predict_model.py --file_path 'path/to/video.mp4'

Miscellaneous

One can try models in action (live) by running the following command:

python demo.py

One can add some arguments as well. Command with default arguments is (example for a simple lstm model):

python demo.py --threshold 0.9 --checkpoint_name 'best.pt' --model_name 'simple_lstm' 

Demo

Video with model predictions