myselfhimanshu/ultron-vision

Computer Vision State Of The Art Intuition Project in Pytorch; WIP

ULTRON VISION MODELS

            

⭐ Star us on GitHub — it helps!

WHAT IS THIS REPO ABOUT?

This repository is my personal research code for exploration of Convolutional Neural Networks. Structured approach to learning and implementing the fundamentals of State of the art vision models.

A Scalable template for PyTorch projects.

Models are built from scratch. No pretrained network used.

Follow below for updates on how it has been built over time. (This repo is work in progress!!!)

UPDATES AND RESULTS

Some codes are available as gist in the form of jupyter notebooks, first few sections have blog posts. Models in these jupter notebooks were trained using Google Colab. From Week-9 you will find the work in this repository.

r"Week-[0-9]*" are clickable

Summary of experiments.

Week-1

• Machine Learning Intuition, Background & Basics
• Python 101 for Machine Learning
• blog

Week-2

• Convolutions, Pooling Operations & Channels
• Pytorch 101 for Vision Machine Learning
• blog

Week-3

• Kernels, Activations and Layers
• blog

Week-4

• Architectural Basics. Finding suitable model architecture for the objective
• MNIST model training
  • parameters used 13,402
  • epochs=20
  • highest test accuracy = 99.46%, epoch = 19th
  • notebook link

Week-5

• Receptive Field : core fundamental concept
• MNIST model training
  • parameters used 7808
  • epochs=15
  • highest test accuracy = 99.43%, epoch = 11th
  • notebook link

Week-6

• BN, Kernels & Regularization
• Mathematics behind Batch Normalization, Kernel Initialization and Regularization
• MNIST model training
  • using L1/L2 regularization with BN/GBN
  • BN : batch normalization
  • GBN : ghost batch normalization
  • best model : BN with L2
    • parameters used 7808
    • epochs=25
    • highest test accuaracy = 99.54%, epoch = 21st
  • notebook link

Week-7

• Advanced Convolution
• Depthwise, Pixel Shuffle, Dilated, Transpose Convolutions
• CIFAR-10 dataset
• Achieve an accuracy of greater than 80% on CIFAR-10 dataset
  • architecture to C1C2C3C40 (basically 3 MPs)
  • total params to be less than 1M
  • RF must be more than 44
  • one of the layers must use Depthwise Separable Convolution
  • one of the layers must use Dilated Convolution
  • use GAP
• Result
  • parameters used 220,778
  • epochs = 20
  • highest test acc = 85.55%
  • notebook link

Week-8

• Receptive Fields and Network Architectures : Resnet Architecture
• Achieve an accuracy of greater than 85% on CIFAR-10 dataset
  • architecture ResNet18
• Result
  • parameters : 11,173,962
  • epoch : 50
  • training acc : 98.65%
  • testing acc : 89.78%
  • notebook link

Week-9

• Data Augmentation using Albumentations
• DNN Interpretability, Class Activation Maps using grad-cam
• Achieve an accuracy of greater than 87% on CIFAR-10 dataset
  • architecture ResNet18
  • Move transformations to Albumentations.
  • Implement GradCam function.
• Result
  • parameters : 11,173,962
  • epoch : 50
  • testing acc : 92.17%
  • work link

Week-10

• Advanced Concepts : Optimizers, LR Schedules, LR Finder & Loss Functions
• Achieve an accuracy of greater than 88% on CIFAR-10 dataset
  • architecture ResNet18
  • Add CutOut augmentation
  • Implement LR Finder (for SGD, not for ADAM)
  • Implement ReduceLROnPlateau
• Result
  • parameters : 11,173,962
  • epoch : 50
  • testing acc : 89.80%
  • work link

Week-11

• Super Convergence
• Cyclic Learning Rates, One Cycle Policy
• Achieve an accuracy of greater than 90% on CIFAR-10 dataset
  • 3Layer-DenseNet
  • Implement One Cycle Policy
• Result
  • parameters : 6,573,130
  • epoch : 24
  • testing acc : 91.02%
  • work link

Week-12

• Object Localization : YOLO
• Use TinyImageNet dataset, create custom data loader with 70/30 split.
• Achieve an accuracy of greater than 50% on TinyImageNet dataset
  • ResNet18
  • One Cycle Policy
• Result
  • parameters : 11,173,962
  • epoch : 30
  • testing acc : 58.35%
  • work link

HARDWARE CONFIGURATION

• GPUs : NVIDIA® GeForce® GTX 1080Ti
• GPU count : 1,2
• vCPUs : 4,8
• Memory : 12 GiB, 24 GiB
• Disk : 80 GiB, 80 GiB
• Genesis Cloud offers GPU cloud computing at unbeatable cost efficiency.

MODULES IMPLEMENTED

• pytorch-transformation
• albumentation-transformation
• data loader using pytorch in-built Datasets class
• custom data loader class
• training class
• inference class
• prediction on single image
• lr-range test, optim lerning rate value finder
• default scheduler implementation
• one-cycle-policy (current default)
• interpret misclassified images using grad-cam
• plots of accuracy, loss, learning_rate graphs wrt iterations
• logging functionality
• loading and saving model checkpoints
• custom configuration file for training model
• data-parallel mode for training on more than 1 GPUs.
• custom loss function
• using weights and biases for logging experiments
• torchstat or torchprof, layer-by-layer profiling of Pytorch models
• Deployment using Flask, EC2 or AWS-Lambda

USE-CASES MODELS IMPLEMENTED FOR

• Image Classification
• Object Detection
• Object Segmentation
• GANs

NETWORK ARCHITECTURES IMPLEMENTED

• Custom Networks
• ResNet
• DenseNet

DATASETS USED

• MNIST : 70,000 28x28 grayscale images in 10 classes; train set : 60,000; test set : 10,000.
• CIFAR10 : 60000 32x32 colour images in 10 classes, with 6000 images per class. 50000 training images and 10000 test images.
• TinyImageNet : 1,10,000 64x64 color images in 200 classes. (70/30) split for train and test data set respectively.

INSTALLATION

// install virtualenv
$ python3 -m pip install --user virtualenv

// create environment
$ python3 -m venv myenv

// activate environment
$ source myenv/bin/activate

// install dependencies from requirements.txt
$ pip install -r requirements.txt

// deactivate environment
$ deactivate

TRAINING ULTRON

In ultron.sh provide ultron.py and path-to-config-file.

// make ultron.sh executable file
$ chmod +x ultron.sh

// train ultron
$ ./ultron.sh

FOLDER STRUCTURE

.
├── agents // define training and validation
│   ├── base.py
│   ├── mnist_agent.py
│   └── cifar10_agent.py
│
├── configs // store networks configuration parameters
│   ├── mnist_config.json
│   └── cifar10_config.json
│
├── data // raw, processed data + test images
│
├── experiments // store checkpoints, logs and outputs for experiment
│   └── cifar10_exp*
│       ├── logs // agent logs
│       ├── stats // training validation scores, plots and images visualization data
│       └── summaries // experiment config file used and network architecture
│
├── infdata // initialize and fetch dataset
│   ├── dataset // defining custom dataset class
│   ├── transformation // custom transformation class
│   └── loader // data loader
│       └── cifar10_dl.py
│
├── inference // define inference agent
│   ├── base.py
│   └── cifar_iagent.py
│
├── logger.py // define the logger
├── losses // custom network losses
│
├── networks // define our network
│   ├── resnet_net.py
│   ├── mnist_net.py
│   └── utils.py
│
├── notebooks // jupyter notebooks for experiments
│   └── cifar10_nb.ipynb
│
├── utils // helper functions
│   ├── lr_finder
│   └── gradcam
│
├── README.md
├── requirements.txt
├── ultron.py
└── ultron.sh

License

Distributed under the GNU GPLv3 License. See LICENSE for more information.

Contact

Your Name - @047himanshu - [email protected]

Project Link: https://github.com/myselfHimanshu/ultron-vision