animesh-007 / midas_internship_task2

Pytorch solution of MIDAS LAB INTERNSHIP Task 2

Solution+Report for MIDAS Internship task 2

This repository contains solution+report for the MIDAS internship task 2.

Installation

The model is built in PyTorch 1.8.1 and tested on Ubuntu 18.04 environment (Python3.7.10, CUDA10.1, cuDNN7.6.3).

For installing, follow these intructions

conda create -n pytorch1.8 python=3.7.10
conda activate pytorch1.8
conda install pytorch=1.8.1 torchvision=0.9.1 cudatoolkit=10.1 -c pytorch
pip install requirements.txt

Network Architecture

The network architecture below is used for all the 3 subtasks by changing the last layer of the architecture.

Model Checkpoints

Pre-trained network weights for each task are uploaded on this link: https://drive.google.com/drive/folders/1CZsiAQ9WqtwY1SMPmv04MGeIOUETbeY7?usp=sharing

Drive directory structure:
subtask1_checkpoint_model_best.pth.tar : weights for task2_1 
subtask2.1_checkpoint_model_best.pth.tar : weights for task2_2_1 
subtask2.2_checkpoint_model_best.pth.tar : weights for task2_2_2 
subtask2.3_checkpoint_model_best.pth.tar : weights for task2_2_3 
subtask3.1_checkpoint_model_best.pth.tar : weights for task2_3_1 
subtask3.2_checkpoint_model_best.pth.tar : weights for task2_3_2 

Task2_1

Change directory to Task2_1 using cd task2_1

Prepare data

• Download data using python download.py
• It will download data for the Task2_1 in the ./downloadeddata directory and will also rename the folders according to the labels in the MNIST.
• Run python split.py for splitting the dataset in 80:20 train-val ratio for training and validating the trained model on the given dataset and save the data in ./data.

Training

• For Training the model from scratch on the Task2_1 dataset. Run python train.py

Results

Method	Epochs	Accuracy
CNN without Scheduler	30	67.94
CNN with CosineAnnealingLR Scheduler	30	68.75

Task2_2

Change directory to Task2_2 using cd task2_2

Prepare data

• Run python process.py for creating a subset from Task2_1 containing only images with digits labels in the ./data directory.

Training

• For Training the model from scratch on the Task2_3 dataset. Run python train.py

Results

Method	Epochs	Accuracy
CNN on MIDAS dataset containing only digits, with a CosineAnnealingLR scheduler	30	66.36
CNN on MNIST dataset with random weights, with a CosineAnnealingLR scheduler.	30	99.39
CNN on MNIST dataset with pretrained weights, with a CosineAnnealingLR scheduler	30	99.34

Task2_3

Change directory to Task2_3 using cd task2_3

Prepare data

• Run python download.py for for downloading the data in the ./data directory.

Training

• For Training the model from scratch on the Task2_3 dataset. Run python train.py

Results

Method	Accuracy
CNN on MIDAS Dataset with random weights.	1.74
CNN on MIDAS Dataset with pretrained weights of MIDAS dataset containing only digits.	10.32