The following are the brief details of the models implemented in this repository
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Assignment 1 has implementation of image classification for MNIST dataset from scratch. It uses the following python Libraries:
numpy
arrays are used for simpler code and faster computationsmatplotlib.pyplot
is used for plotting images and visualizing data and resultssklearn.model_selection.train_test_split
is used for splitting the dataset into train and test dataset
The final accuracy of the trained model has an accuracy of 95.067 %.
The above libraries are used in all the model implementations. -
Assignment 2 has implementation of image classification for MNIST dataset. It uses
torch
library for easier and faster implementation of Neural networks.The final accuracy of the trained model has an accuracy of 97.723 %.
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Assignment 3 has implementation of image classification for CIFAR-10 dataset using Convolutional Neural Networks. It uses
torch
forDataloader
class as well as fortorch.nn.Module
class for neural network.torchvision
is used for transforming images.
The final accuracy of the trained model is 66.242 %. -
Assignment 4 is about image segmentation on Caravana dataset. It implements U-Net for the same. It also used
torch
andtorchvision
libraries.
The final accuracy of the trained model has an accuracy of 89.714 %.