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Emotion Image Classification V2

Developed by jhoppanne
A fine-tuned emotion image classification model based on Google's ViT model, achieving an accuracy of 59.38% on the validation set.
Downloads 2,176
Release Time : 5/30/2024

Model Overview

This model is a fine-tuned emotion image classification model based on Google's ViT architecture, primarily used for classifying and recognizing emotions in images.

Model Features

Based on ViT Architecture
Uses Google's Vision Transformer (ViT) as the base model, offering excellent image feature extraction capabilities.
Emotion Classification
Fine-tuned specifically for image emotion recognition tasks, suitable for analyzing emotional content in images.
Moderate Accuracy
Achieves an accuracy of 59.38% on the validation set, suitable for general emotion recognition applications.

Model Capabilities

Image classification
Emotion recognition
Visual feature extraction

Use Cases

Social Media Analysis
User-uploaded Image Emotion Analysis
Analyzes the emotional tendencies in images uploaded by social media users
Can recognize 59.38% of emotion categories
Market Research
Ad Image Emotional Impact Assessment
Evaluates the emotional reactions elicited by advertisement images
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