EVA 4.0 (Extensive Vision AI)

This contains the solutions to the assignments given in The School of AI - Extensive Vision AI 4.0

Website: https://satyajitghana.github.io/TSAI-DeepVision-EVA4.0/

[i'd prefer to use the website link]

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1. Python Basics

   This describes the very basics of Python, i would recommend binge watching Raymond Hettinger's YouTube Videos, as much as you can, also learn list comprehension, slicing, partial, functools, functional programming, classes, MRO, decorators, lambdas, python 3 typing

   Also here i learnt the basics of how a Neural Network learns, how the different channels are formed, what are kernels, how does the DNN make sense of the input it gets, and more !

   Have you ever wondered, like why do we only use 3x3 kernels ? i had always wondered, why not 5x5 ? 7x7 ? in this session i learnt why, and how does even the kernel size matter ? why is it only odd numbers ? and why only squares ? and why is 3x3 kernel used twice same as a 5x5 kernel, what is receptive field ?

2. Neural Architecture

   This describes the basic neural network architecure, see its page for details

3. PyTorch 101

   Basic Pytorch architecture for working with neural networks, introduces you to nn.Module, optimizers, forward and backward pass, datasets, how to apply simple augmentation.

4. Architecture Basics

   Here we had to train MNIST to get 99.4% accuracy with some given contraints, was quite fun to do this, got me out of the noob shell of what a neural network actually is and what it does, because i had to write code manually, not copy paste any more.

5. Coding Drill

   This was very important to realise the basic steps required to make a neural network and then go on to optimize it, to get the perfect model size and accuracy, very important, please see its documentation, and all the notebooks

6. Normalization Regularization

   This assigment focuses on the importance of normalization and regularization in neural networks, aim was to also get > 99.4% accuracy in less than 40 epochs for MNIST with limited model parameters

7. Advanced Convolution

   Here i was introduced to various convolution types you can add in the network, we were given a custom network which we had to implement, also we had to make a custom library of python to support and ease the process of NN building and training, this was quite a eye openener for me, i made my first python package !, also i learnt the value of time contraints, since we have to finish the assignments within a week, along with my college work it was burdening, or maybe i was procrastinating?

8. Advanced Architecture

   Here i was introduced to ResNet18 and CIFAR dataset with aim to get 85% test accuracy

9. Model Diagnostics

   This was amazing, i was introduced to GradCAM, and i myself learnt about saliency map, this was something i also used in my college group project, it's used to diagnosing if the model is actually learning anything from the dataset. Quite a nifty feature, eh ?

10. Advanced Training

    Here i had to use LR Finder to find the best learning rate for the model, i also used OneCyclePolicy for faster convergence. i reached 92.03% accuracy in CIFAR using ResNet18

11. Super Convergence

    A custom architecture was implemented here, along with that i used OneCycleLR to improve the convergence of the NN

12. Tiny Image Net

    Here we had to do image annotation and collect doggo dataset.

    Also i had to make a custom dataloader and dataset class in PyTorch to support TinyImageNet, and then train a model to reach 60% accuracy, which i failed to, but still i learnt from my mistakes ! the one who doesnt make mistakes does really make anything does he ?

13. YOLOV3

    I was introduced to YOLO, something i always wondered how it exactly worked, and why is it called YOLO ? now i understand why. and people complain about the FPS of YOLO, now i know why, they dont pay attention to the anchor boxes, which is very important and its different for different datasets.

    I made a custom Bugs Bunny detector using the YOLO architecture :) i like my detector :) i like bugs bunny :) i custom collected bugs bunny images, self annotated about 600 images of my bunny and trained the network for like 5 hours on a Tesla P100

14. RCNN

    I was introduced to RCNN family and SSD, but that wasnt the main thing here.

    The main thing was that i created a dataset containing 1.2M images ! do you understand how crazy that is ? this was a pre-req for the upcoming CapStone project which will be to do object segmentation and also monocular depth estimation both at the same time.

15. CapStone Project

    This was the final project, please see its README, i spent a lot of time writing code and documenting it.

    I'm sure you'll be impressed by this one ;)

Star History

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