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Fruits

Developed by hgarg
An image classification model built with PyTorch and HuggingPics, specifically designed to recognize different types of fruits.
Image Classification
Transformers
#High-precision image classification#Multi-category fruit recognition#PyTorch lightweight model
Downloads 43
Release Time : 3/2/2022

Model Overview

This model can accurately classify images of common fruits such as apples, bananas, mangoes, oranges, and tomatoes, achieving an accuracy of 97.32%.

Model Features

High Accuracy
Achieves 97.32% accuracy on fruit classification tasks.
Ease of Use
Easily create custom image classifiers through the HuggingPics framework.
Multi-category Recognition
Supports classification and recognition of multiple common fruits.

Model Capabilities

Image Classification
Fruit Recognition
Multi-category Classification

Use Cases

Retail & Inventory Management
Automatic Fruit Sorting System
Used in supermarkets or fruit stores to automatically identify and classify fruits.
Improves inventory management efficiency.
Agricultural Applications
Fruit Quality Inspection
Automatically identifies and classifies fruits on agricultural production lines.
Enhances sorting efficiency and accuracy.
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