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Skincare Detection

Developed by tuphamdf
An image classification model fine-tuned based on Google's ViT architecture for skincare detection tasks, achieving a validation accuracy of 86.48%
Image Classification
Transformers
Open Source License:Apache-2.0#Skincare Recognition#High Accuracy Classification#ViT Vision Model
Downloads 409
Release Time : 3/1/2024

Model Overview

This model is a fine-tuned version based on the Vision Transformer (ViT) architecture, specifically designed for skincare-related image classification tasks. It demonstrates high classification accuracy on the evaluation set.

Model Features

High Accuracy
Achieves a classification accuracy of 86.48% on the evaluation set
Based on ViT Architecture
Utilizes the Vision Transformer architecture with powerful image feature extraction capabilities
Efficient Fine-tuning
Fine-tuned based on a pre-trained model, ensuring high training efficiency

Model Capabilities

Skincare Image Classification
Visual Feature Extraction
Multi-category Image Recognition

Use Cases

Beauty and Skincare
Automatic Skincare Product Classification
Automatically identifies and classifies different types of skincare products
Accuracy: 86.48%
Retail Product Recognition
Used for automatic product identification and classification in retail scenarios
Quality Control
Product Packaging Inspection
Detects whether skincare product packaging is correct
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