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Asl Detection Yolov5

Developed by niki-stha
A YOLOv5-based object detection model specifically designed for recognizing American Sign Language (ASL) gestures
Downloads 15
Release Time : 6/22/2023

Model Overview

This model is trained using the YOLOv5 architecture, capable of efficiently and accurately detecting American Sign Language gestures, suitable for sign language recognition and translation applications.

Model Features

High-precision Detection
Achieves an excellent mAP@0.5 score of 0.985 on the validation set
YOLOv5 Architecture
Utilizes the efficient YOLOv5 object detection architecture, balancing speed and accuracy
Easy to Use
Provides a simple Python interface for quick deployment and integration

Model Capabilities

Sign language gesture detection
Real-time object recognition
Image analysis

Use Cases

Accessibility Technology
Sign Language Translation System
Build real-time sign language translation applications to assist communication for the deaf and mute
Can accurately recognize multiple ASL gestures
Educational Technology
Sign Language Learning Assistant
Used in sign language learning applications to provide real-time feedback
Helps students master gestures correctly
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