Real time classification of digits (0-9) using openCV.
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Updated
Nov 27, 2020 - Python
Real time classification of digits (0-9) using openCV.
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
Implementation of LeNet 5 using PyTorch for the MNIST dataset.
Experiment: LeNet-5 (MNIST) using PyTorch
~99.50% accuracy on MNIST in PyTorch
A basic implementation of CNN (LeNet) both without libraries and with Tensorflow, Keras.
Traffic Sign classifier based on TensorFLow
A Pytorch implementation of a customized LeNet-5 algorithm designed to give best results on the classic MNIST dataset.
A Lenet-5 convolutional neural network to classify traffic signs
Implementing LeNet5 to classify digits in MNIST dataset
Pytorch implementation of LeNet5 architecture for Image Classification.
Image recognition on Persian digits with LeNet-5 neural network.
Contains template codes of various deep learning models
MNIST is the de facto “hello world” dataset of computer vision. In this competition, our goal is to correctly identify digits from a dataset of handwritten images.
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