Detection Train

Training a sign language detection model

AI & Machine LearningPythonMIT

Abstract

Detection Train is an open-source AI & Machine Learning project. Training a sign language detection model. This is a fork of the TensorFlow implementation of the model proposed in Real-Time Sign Language Detection using Human Pose Estimation, published in SLRTP 2020. It is built using Python. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

This is a fork of the TensorFlow implementation of the model proposed in Real-Time Sign Language Detection using Human Pose Estimation, published in SLRTP 2020.

In this fork, we add data loading and training for Holistic pose estimation.

This model is used in the Real-TIme Sign Language Detection for Videoconferencing demo published in ECCV 2020.

2. Objective

Training a sign language detection model

This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

Python

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/sign-language-processing/detection-train.git
cd detection-train
python -m train --dataset_path="data.tfrecord" --device="/GPU:0"

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by sign-language-processing and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

Want to build this as your internship project?

Work on an AI & Machine Learning project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.

Apply for AI & Machine Learning Internship