Thumbs Up / Thumbs Down Gesture Classification

This project demonstrates how to build a custom deep learning classification model to detect thumbs-up and thumbs-down gestures using the Jetson Nano (or any OS: Windows, Mac, Linux). The workflow is simple: Capture dataset → Train model → Test with live camera feed, teaching the complete deep learning pipeline from scratch. What makes this project unique is that it focuses on creating your own dataset, not just using a pre-trained model — making it easily extendable for gesture-controlled robots, automation, and human–machine interaction.

Features

Build your own dataset from live camera input
Train a deep learning model with just few gestures
Real-time classification (Thumbs-Up / Thumbs-Down)
Works on Jetson Nano, Windows, macOS, and Linux
You can extend the model to detect other gestures

Components Used

Jetson Nano — for edge inference
Python
Jupyter Notebook / JupyterLab
PyTorch / Torchvision — deep learning
OpenCV — camera integration
Pillow (PIL) — image handling
NumPy — data processing

Learning Milestones (What this repo teaches you)

Creating custom datasets directly from camera feed
Training a CNN / image classifier using PyTorch
Working with OpenCV for real-time camera capture
Data preprocessing (resize, normalization, transform pipelines)
Saving and loading model weights
Running real-time inference with webcam on Jetson Nano

Working

Working

Code & Installation

The complete source code is available in the GitHub repository.

View on Github –>

My Learnings

How to capture and store dataset folders programmatically
How to train custom image classification models using PyTorch
How to manage train/test notebooks effectively
Using OpenCV to connect camera for live inference
Understanding transforms (resize, normalize, tensor conversion)
How ML models react to small datasets + augmentation techniques
Running trained models on Jetson Nano’s GPU accelerated PyTorch
This project helped me connect the dots between data → training → model → inference.