https://github.com/shan18/eva4
Deep Learning Projects in TSAI - Extensive Vision AI 4
https://github.com/shan18/eva4
cifar10 computer-vision convolutional-neural-networks deep-learning eva4 jupyter-notebook mnist python3 pytorch torchvision tsai
Last synced: 3 months ago
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Deep Learning Projects in TSAI - Extensive Vision AI 4
- Host: GitHub
- URL: https://github.com/shan18/eva4
- Owner: shan18
- Created: 2020-02-10T08:37:42.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-07-06T13:19:59.000Z (over 5 years ago)
- Last Synced: 2025-06-06T03:41:26.414Z (4 months ago)
- Topics: cifar10, computer-vision, convolutional-neural-networks, deep-learning, eva4, jupyter-notebook, mnist, python3, pytorch, torchvision, tsai
- Language: Python
- Homepage:
- Size: 40.6 MB
- Stars: 3
- Watchers: 1
- Forks: 7
- Open Issues: 0
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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](04%20-%20Architectural%20Basics/).
### 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](05%20-%20Coding%20Drill%20Down/).
### Regularization
Applying L1 and L2 regularization on the final model trained in Session 5.
To see the code go [here](06%20-%20Regularization/).
### Advanced Convolutions
Reaching a test accuracy of 80% on CIFAR-10 dataset using advanced convolutions.
To see the code go [here](07%20-%20Advanced%20Convolutions/).
### Receptive Fields and Network Architecture
Reaching a test accuracy of 85% on CIFAR-10 dataset with ResNet18 model.
To see the code go [here](08%20-%20Receptive%20Fields%20and%20Network%20Architecture/).
### 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](09%20-%20Data%20Augmentation%20and%20Grad%20Cam/).
### 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](10%20-%20LR%20Finder%20and%20Reduce%20LR%20on%20Plateau/).
### 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](11%20-%20Super%20Convergence/).
### 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](12%20-%20Tiny-ImageNet%20and%20YOLO%20v2%20Anchor%20Boxes/).
### Object Detection with YOLO v3
Using transfer learning to detect a custom object using YOLO v3.
To see the code go [here](13%20-%20Object%20Detection%20with%20YOLO%20v3/).
### 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](14%20-%20Segmentation%20and%20Depth%20Estimation%20Dataset%20Creation/).
### 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](15%20-%20Image%20Segmentation%20and%20Depth%20Estimation/).