FRVEG (PRODUCT)

This model is especially trained for 65 specific fruits and vegetables to predict them that are given in zip.

Product Specifications

DataSets Trained:

Algorithm:

CNN

Description:

This model is especially trained for 65 specific fruits and vegetables to predict them that are given in zip.

Status:

Tested & Verified

Framework:

keras

License:

MIT

Seller Information

Team aimodelplace.com IRON

(2 Reviews)

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Description

Overview

This high-precision computer vision model is specifically engineered to recognize and classify 65 distinct types of fruits and vegetables. Built using a deep Convolutional Neural Network (CNN), it handles variations in lighting, orientation, and background noise effectively.

Key Features

Extensive Library: Identifies 65 categories (Full list included in the technical docs).
Real-time Ready: Optimized for low-latency inference on standard CPU/GPU setups.
Preprocessed Data: Trained on a meticulously cleaned dataset for high F1-score performance.

Technical Stack

Algorithm	CNN (Convolutional Neural Network)
Framework	Keras / TensorFlow
Input Size	224x224 RGB Images

Best Use Cases

Ideal for Smart Retail (automated checkout), Agricultural sorting, or Diet/Calorie tracking applications.

Live Deployment Sandbox

Online

API Integration Guide

curl --location 'https://api.aimodelplace.com/api/v1/predict' \
--header 'Authorization: Bearer YOUR_API_KEY' \
--form 'model_slug="frveg_1"' \
--form 'file=@"/path/to/your/image.png"'

Successful response format (JSON):

{
    "status": "success",
    "tokens_consumed": 10,
    "balance_remaining": 565,
    "recognized_object": "apple"
}

For more detailed parameters and SDK examples, visit our Full API Documentation.