Simple Flask web app able to distinguish between real world images of Apples, Bananas, Oranges with predicting weather the fruit in image is Fresh or Rotten with respective probabilities on real-time hosted web service (Heroku)

🍎 Fruit & vegetable image classifier using TensorFlow CNN. 18 categories, Streamlit web app, Jupyter training notebooks, modular Python code. Learn computer vision & deep learning. Automated preprocessing, metrics visualization, easy deployment. Educational ML project with clean architecture.

This project aims to classify different types of fruits using deep learning. The objective is to build a model that can accurately identify the type of fruit based on images.

AI-powered fruit ripeness classification combining computer vision, feature selection, and machine learning algorithms

A computer vision project for fruit detection and classification using YOLO object detection models in a Jupyter Notebook. Demonstrates data preparation, training, and inference with YOLO for fruit recognition tasks.

Using an algorithm that allows it to distinguish fruit images by type of fruit.

Image classification : Detecting the fresh and spoiled fruits for the IEEE event.

Using Alexnet Architecture for Multiclass Fruit Classification

Fruit Classification using CNN

Computer vision project for classifying fruit ripeness using deep learning

Fresh rotten fruit classification using mobilenetv2 architecture

Project for Computer Vision

A dual-headed deep learning model built using TensorFlow and Keras to classify fruit type (Apple, Banana, Guava, Orange) and their quality condition (Good or Bad) from images. The system includes Grad-CAM-based visual explanations and a responsive Streamlit web interface for real-time predictions using uploaded images or webcam input.

Fruit Classifier is a machine learning project that leverages deep learning techniques to classify different types of fruits.

A web-based Fruit Classifier App built with Streamlit that predicts fruit species based on features like diameter, weight, and RGB color values.

Fruit Ripeness Classifier, ensembling deep learning CNN and shallow learning. Consists of two stage: (1) fruit classifier (2) ripe classifier

AI-powered fruit freshness classification using deep learning and Streamlit

Deep Learning model for fruit classification using CNNs, designed for embedded Linux deployment. Built with TensorFlow/Keras on Kaggle's Fruits-360 dataset. Features: data augmentation, dropout regularization, TFLite conversion, confusion matrix analysis, and detailed performance metrics per class.

A lightweight and efficient computer vision solution for classifying various fruits using MobileNetV2 and transfer learning. Includes robust data preprocessing pipelines, clear repository structure, and detailed documentation for easy collaboration.