https://github.com/rvk007/eva4

Solution to the assignments in the EVA4 course
https://github.com/rvk007/eva4

ai cnn computer-vision deep-learning depth-estimation eva4 jupyter-notebook lr-finder object-detection python3 pytorch pytorch-implementation segmentation

Last synced: 12 days ago
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Solution to the assignments in the EVA4 course

• Host: GitHub
• URL: https://github.com/rvk007/eva4
• Owner: rvk007
• Created: 2020-02-10T11:46:50.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2024-05-03T19:54:31.000Z (9 months ago)
• Last Synced: 2024-05-03T20:59:40.367Z (9 months ago)
• Topics: ai, cnn, computer-vision, deep-learning, depth-estimation, eva4, jupyter-notebook, lr-finder, object-detection, python3, pytorch, pytorch-implementation, segmentation
• Language: Jupyter Notebook
• Homepage:
• Size: 66.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 7
• Metadata Files:
  • Readme: README.md

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README

        
# TSAI - Extensive Vision AI 4

This repository contains the solutions to the assignments of the **EVA4** course conducted by _The School of AI_.

## Contents

### Architectural Basics (MNIST Classification)

Reaching 99.4% accuracy on the MNIST test dataset with a model having less than 20,000 parameters and has been trained for less than 20 epochs.

To see the code go [here](S4/).

### Coding Drill Down

Reaching 99.4% accuracy on the MNIST test dataset with a model having less than 10,000 parameters and has been trained for less than 15 epochs.

To see the code go [here](S5/).

### Regularization

Applying L1 and L2 regularization on the final model trained in Session 5.

To see the code go [here](S6/).

### Advanced Convolutions

Reaching a test accuracy of 80% on CIFAR-10 dataset using advanced convolutions.

To see the code go [here](S7/).

### Receptive Fields and Network Architecture

Reaching a test accuracy of 85% on CIFAR-10 dataset with ResNet18 model.

To see the code go [here](S8/).

### Data Augmentation and Grad Cam

Reaching a test accuracy of 87% on CIFAR-10 dataset with ResNet18 model using Grad Cam and various data augmentation techniques.

To see the code go [here](S9/).

### LR Finder and Reduce LR on Plateau

Reaching a test accuracy of 88% on CIFAR-10 dataset with ResNet18 model using LR Finder and Reduce LR on Plateau.

To see the code go [here](S10/).

### Super Convergence

Reaching a test accuracy of 90% on CIFAR-10 dataset custom ResNet model using One Cycle Policy for Learning Rate.

To see the code go [here](S11/).

### Tiny-ImageNet and YOLO v2 Anchor Boxes

Reaching a test accuracy of 50% on Tiny-ImageNet dataset with ResNet18 model and finding the anchor boxes for YOLO v2 using K-Means Clustering algorithm.

To see the code go [here](S12/).

### Object Detection with YOLO v3

Using transfer learning to detect a custom object using YOLO v3.

To see the code go [here](S13/).

### Segmentation and Depth Estimation Dataset Creation

Creating a dataset with 400,000 images for image segmentation and depth estimation.

To see the code go [here](S14/).

### Image Segmentation and Depth Estimation

Creating a model which can perform image segmentation and depth estimation on a custom dataset.

To see the code go [here](S15/).