H

Hq Fer2013notestaugm

Developed by Piro17
A fine-tuned image classification model based on ViT architecture, excelling on the FER2013 dataset
Downloads 17
Release Time : 2/19/2023

Model Overview

This model is a fine-tuned version of the google/vit-base-patch16-224-in21k pre-trained model for image classification tasks, primarily used for facial expression recognition

Model Features

High Accuracy
Achieves 69.98% accuracy on the FER2013 dataset
Based on ViT Architecture
Utilizes Vision Transformer architecture with powerful image feature extraction capabilities
Fine-tuned
Performance gradually improved through 10 training epochs

Model Capabilities

Image Classification
Facial Expression Recognition
Emotion Analysis

Use Cases

Affective Computing
Facial Expression Recognition
Recognizes facial expressions of individuals in images
Achieves 69.98% accuracy on the FER2013 dataset
Human-Computer Interaction
Emotional Feedback System
Used to detect user emotional states
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