README.md

monk_v1

Website

Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.

   

Table of Contents

• 1. Sample Showcase
• 2. Installation
• 3. Study Roadmaps, Examples, and Tutorials
• 4. Documentation
• 5. TODO

Sample Showcase

Create an image classification experiment.

• Load foldered dataset
• Set number of epochs
• Run training

ptf = prototype(verbose=1)
ptf.Prototype("sample-project-1", "sample-experiment-1")
ptf.Default(dataset_path="./dataset_cats_dogs_train/", 
                model_name="resnet18", freeze_base_network=True, num_epochs=2)
ptf.Train()

Inference

img_name = "./monk/datasets/test/0.jpg";
predictions = ptf.Infer(img_name=img_name, return_raw=True);
print(predictions)

Compare Experiments

• Add created experiments with different hyperparameters
• Generate comparison plots

ctf = compare(verbose=1);
ctf.Comparison("Sample-Comparison-1");
ctf.Add_Experiment("sample-project-1", "sample-experiment-1");
ctf.Add_Experiment("sample-project-1", "sample-experiment-2");
    .
    . 
    .
ctf.Generate_Statistics();

Installation

Support for

• OS
  • Ubuntu 16.04
  • Ubuntu 18.04
  • Mac OS
  • Windows
• Python
  • Version 3.6
  • Version 3.7
• Cuda
  • Version 9.0
  • Version 9.2
  • Version 10.0
  • Version 10.1

For Installation instructions visit: Link

Study Roadmaps

• 1. Getting started with Monk
• 2. Python sample examples
• 3. Image Processing and Deep Learning
• 4. Transfer Learning
• 5. Image classification zoo

Documentation

• 1. List of available models, layers, blocks, optimizers
• 2. Functional Documentation (Will be merged with Latest docs soon)
  • Main Prototype Functions
    • Mxnet Backend
    • Pytorch Backend
    • Keras Backend
    • Comparison
  • System Functions
  • Mxnet Backend base Functions
  • Pytorch Backend base Functions
  • Keras Backend base Funtions
• 3. Features and Functions (In development):
  • Introduction
  • Setup
  • Quick Mode
  • Update Mode
  • Expert Mode
  • Hyper Parameter Analyser
  • Compare Experiments
  • Resume Training
• 4. Complete Latest Docs (In Progress)

TODO-2020

TODO-2020 - Features

• Model Visualization
• Pre-processed data visualization
• Learned feature visualization
• NDimensional data input - npy - hdf5 - dicom - tiff
• Multi-label Image Classification
• Custom model development

TODO-2020 - General

• Incorporate pep coding standards
• Functional Documentation
• Tackle Multiple versions of libraries
• Add unit-testing
• Contribution guidelines

TODO-2020 - Backend Support

• Tensorflow 2.0
• Chainer

TODO-2020 - External Libraries

• TensorRT Acceleration
• Intel Acceleration
• Echo AI - for Activation functions

Copyright

Copyright 2019 onwards, Tessellate Imaging Private Limited Licensed under the Apache License, Version 2.0 (the "License"); you may not use this project's files except in compliance with the License. A copy of the License is provided in the LICENSE file in this repository.