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: 2 months 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 (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-07-25T10:58:24.000Z (almost 2 years ago)
- Last Synced: 2025-01-08T17:58:45.248Z (over 1 year 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: 1
- Watchers: 1
- Forks: 0
- Open Issues: 8
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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](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/).