Reconnaissance des fruits en utilisant les modéles de deep learning: Resnet, Vgg16, MobileNetV2, CNN personalisé
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Updated
Feb 23, 2021
Reconnaissance des fruits en utilisant les modéles de deep learning: Resnet, Vgg16, MobileNetV2, CNN personalisé
A boring 33 class fruit classifier.
This project focuses on building and improving a fruit recognition model using convolutional neural networks (CNNs) to accurately classify various fruit types, enhancing generalization with advanced model architecture and regularization techniques.
CNN is used to build a deep learning model for the prediction of fruits. Total of 31 different fruits classes are there
Classification using fruits360 from kaggle, and applying ML techniques
Image segmentation model for checking apple quality using UNet model architecture on TensorFlow along with AWS SageMaker deployment.
Robust fruit detection system using YOLOv4.
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-Driven Smart Fruit Classifier
This is the same one as before, but I use deep learning
This is my assignment 3 recognition system
Fruit Classification using CNN
Fruit image classifier made with Keras VGG19 transfer learning and flask.
Artificial Neural Network in Web Assembly, C++ Core & Interactive Website.
Machine Learning with Streamlit Python
This repository contains the code related to the paper "Stop overkilling simple tasks with black-box models, use more transparent models instead"
Ripeness Palm Oil with deep learning model
Using Alexnet Architecture for Multiclass Fruit Classification
Página de apresentação do projeto de frutas e vegetais
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