🚗 Driver Drowsiness Detection with Deep Learning (PoC) 🧠 A personal experiment comparing CNN + MobileNetV2 and YOLOv11 for real-time drowsiness detection. Focused on evaluating accuracy and efficiency to explore AI’s potential in preventing fatigue-related accidents. 😴⚡ A hands-on dive into safety-driven computer vision.

Real-time Rock–Paper–Scissors game powered by YOLOv11—featuring hand gesture detection, cheat prevention, and celebratory winner overlays

🤟 YOLOv11 Sign Language Detection – Detect hand gestures & alphabets in images & videos using AI 🚀 | Built with Python, Ultralytics YOLOv11, Roboflow & Google Colab 🖥️📸

This project uses YOLOv8 as the framework and the hand dataset for model training. Having detailed Readme documentation and project structure documentation, it is highly user-friendly for beginners who are new to YOLO.

Contribute to the open source community,A hand detection model base on yolo11n

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Fine tuned YOLOv11 model for detecting 21 distinct keypoints on hand for accurate gesture detection

Structured study repository covering the full machine learning curriculum — from linear regression and ensemble methods to CNNs, object detection with YOLOv3/YOLOv11, GANs, and genetic algorithms. Hands-on Jupyter notebooks with a Dockerized JupyterLab environment.

Hand-detection model fine-tuned YOLOv11 on hands dataset

Conference AICCSA 2025

A project leveraging YOLOv11 for multi-handwriting detection and PyTorch-based CNN for handwriting classification in multi-writer documents.

Real-time hand detection with custom-trained YOLOv11 model

This project leverages the power of YOLOv11, PyTorch, and OpenCV to detect and recognize Malaysian Hand Sign Language in real-time from camera feeds or images. Consists of 'SAYA' 'AWAK' 'MAKAN' 'MINUM' 'MAAF' 'SALAH' 'TOLONG'

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Real-time sign language detection system using YOLOv11, trained on custom gesture dataset. Detects and classifies hand gestures for improved communication accessibility.

This repository contains a real-time Indian Sign Language Detection application developed using the YOLOv11 model. The goal of the project is to make sign language communication more accessible by converting hand gestures into meaningful labels using deep learning.