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
Pytorch implementation of LeNet5 architecture for Image Classification.
Image recognition on Persian digits with LeNet-5 neural network.
A Lenet-5 convolutional neural network to classify traffic signs
Implementing LeNet5 to classify digits in MNIST dataset
A LeNet-5 implementation using C language and FPGA, obtaining more performance (Hardware) together with greater versatility (Software), uniting the two worlds. Hardening the Software and Softening the Hardware, to something in between, like Molten Iron, so a Moltenware implementation.
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.
Implementation of the LeNet-5 model proposed by Yann Le Cun in 1998
Batch normalization from scratch on LeNet using tensorflow.keras on mnist dataset. The goal is to learn and characterize batch normalization's impact on the NN performance.
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