Open-source Indian Sign Language Image Classification Model - Based on Google Architecture, Accurately Identifies with Over 99% Accuracy

Indian Sign Language Classification

Developed by Hemg

Fine-tuned Indian Sign Language image classification model based on Google Vision Transformer (ViT) architecture, achieving 99.05% accuracy

Image Classification

Transformers

Open Source License:Apache-2.0 #Sign Language Recognition #High Accuracy #Vision Transformer

Downloads 167

Release Time : 3/15/2024

Model Overview

This model is a vision classification model for Indian Sign Language recognition, fine-tuned based on the pre-trained ViT architecture, specifically designed for recognizing sign language images

Model Features

High Accuracy

Achieves 99.05% classification accuracy on the evaluation set

Based on ViT Architecture

Uses Vision Transformer architecture, suitable for image classification tasks

Fine-tuned Model

Fine-tuned based on the google/vit-base-patch16-224-in21k pre-trained model

Model Capabilities

Sign Language Image Classification

Visual Feature Extraction

Multi-class Image Recognition

Use Cases

Accessibility Technology

Sign Language Translation System

Used to build real-time sign language recognition and translation systems

High-accuracy sign language classification

Educational Assistance Tool

Sign language recognition tool to help hearing-impaired individuals learn and communicate

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2504	1.0	2137	0.0731	0.9800
0.0519	2.0	4274	0.0327	0.9905

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Indian Sign Language Classification

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Indian-sign-language-classification

🚀 Quick Start

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

📄 License