Real time classification of digits (0-9) using openCV.

A simple CNN based on LeNet5 implemented in Julia 1.6.

Build a DNN Model for MNIST Dataset Provided. Expected Test accuracy is 97 %. The model build should have the number of Neural Network parameters smaller than 1 Lakh

Handwriting Digit Recognizer

Image-processing-of-intensive-reading-series-of-papers

simple project to train a model to recognize the persian letters in an image.

Traffic Sign classifier based on TensorFLow

MNIST Classifier using LeNet-5 Architecture

A Pytorch implementation of a customized LeNet-5 algorithm designed to give best results on the classic MNIST dataset.

The neural network inspired with LeNet5 model.

Implementation of LeNet-5 over MNIST Dataset using PyTorch from Scratch, presenting an accuracy of ~99%

Implementation of LeNet 5 using PyTorch for the MNIST dataset.

Experiment: LeNet-5 (MNIST) using PyTorch

This project utilizes a convolutional network to identify 9 different kinds of skin cancers including melanoma, nevus, and more. The model is trained on over 2,200 pictures of various skin cancers based off of this dataset. This model implements fundamental computer vision and classification techniques and includes a step-by-step implementation.

~99.50% accuracy on MNIST in PyTorch

Covid-19 detection using chest Xray images with LeNet-5 architecture

Machine Learning

Traffic sign classifier using OpenCV, LeNet-5, AlexNet

A Lenet-5 convolutional neural network to classify traffic signs