Vit-Facial-Expression-Recognition Open Source Model - Efficient and Accurate Facial Expression Recognition with Over 91% Accuracy

Vit Facial Expression Recognition

Developed by Alpiyildo

A facial expression recognition model based on ViT architecture, fine-tuned on the imagefolder dataset with an accuracy of 91.77%

Face-related

Transformers

#High-precision facial expression recognition #ViT architecture #Real-time emotion analysis

Downloads 581

Release Time : 7/9/2024

Model Overview

This model uses the Vision Transformer (ViT) architecture, specifically designed for facial expression recognition tasks, capable of accurately classifying facial expressions in input images.

Model Features

High Accuracy

Achieves 91.77% accuracy on the evaluation set

Based on ViT Architecture

Uses the Vision Transformer architecture, suitable for image classification tasks

Fine-tuned Version

Optimized through fine-tuning for specific tasks on the base model

Model Capabilities

Facial expression recognition

Image classification

Real-time expression analysis

Use Cases

Human-Computer Interaction

Emotion Recognition System

Used to identify user facial expressions to determine emotional states

91.77% accuracy

Psychological Research

Emotional Response Analysis

Used for recording and analyzing emotional responses of subjects in psychological experiments

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5315	0.8909	100	0.2603	0.9184
0.5202	1.7817	200	0.2583	0.9181
0.4912	2.6726	300	0.2609	0.9171

Property	Details
Base Model	motheecreator/vit-Facial-Expression-Recognition
Tags	generated_from_trainer
Datasets	imagefolder
Metrics	accuracy
Model Name	vit-Facial-Expression-Recognition
Task	Image Classification (image - classification)
Dataset Name	imagefolder
Dataset Type	imagefolder
Dataset Config	default
Dataset Split	None
Dataset Args	default
Accuracy	0.9176650877681806

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Vit Facial Expression Recognition

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 vit-Facial-Expression-Recognition

🚀 Quick Start

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

🔧 Technical Details

Training procedure

Training hyperparameters

Training results

Framework versions

📦 Additional Information