Awesome

Monk - A computer vision toolkit for everyone

Why use Monk

• Issue: Want to begin learning computer vision
  • <b> Solution: Start with Monk's hands-on study roadmap tutorials</b>
• Issue: Multiple libraries hence multiple syntaxes to learn
  • <b> Solution: Monk's one syntax to rule them all - pytorch, keras, mxnet, etc </b>
• Issue: Tough to keep track of all the trial projects while participating in a deep learning competition
  • <b> Solution: Use monk's project management and work on multiple prototyping experiments</b>
• Issue: Tough to set hyper-parameters while training a classifier
  • <b> Solution: Try out hyper-parameter analyser to find the right fit </b>
• Issue: Looking for a library to build quick solutions for your customer
  • <b> Solution: Train, Infer and deploy with monk's low-code syntax </b>

Create real-world Image Classification applications

...... For more check out the Application Model Zoo!!!!

How does Monk make image classification easy

• Write less code and create end to end applications.
• Learn only one syntax and create applications using any deep learning library - pytorch, mxnet, keras, tensorflow, etc
• Manage your entire project easily with multiple experiments

For whom this library is built

• Students
  • Seamlessly learn computer vision using our comprehensive study roadmaps
• Researchers and Developers
  • Create and Manage multiple deep learning projects
• Competiton participants (Kaggle, Codalab, Hackerearth, AiCrowd, etc)
  • Expedite the prototyping process and jumpstart with a higher rank

Table of Contents

• Sample Showcase
• Installation
• Study Roadmaps, Examples, and Tutorials
• Documentation
• TODO

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Sample Showcase - Quick Mode

Create an image classifier.

#Create an experiment
ptf.Prototype("sample-project-1", "sample-experiment-1")

#Load Data
ptf.Default(dataset_path="sample_dataset/", 
             model_name="resnet18", 
             num_epochs=2)
# Train
ptf.Train()

Inference

predictions = ptf.Infer(img_name="sample.png", return_raw=True);

Compare Experiments

#Create comparison project
ctf.Comparison("Sample-Comparison-1");

#Add all your experiments
ctf.Add_Experiment("sample-project-1", "sample-experiment-1");
ctf.Add_Experiment("sample-project-1", "sample-experiment-2");
   
# Generate statistics
ctf.Generate_Statistics();

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Installation

• CUDA 9.0          : pip install -U monk-cuda90
• CUDA 9.0          : pip install -U monk-cuda92
• CUDA 10.0        : pip install -U monk-cuda100
• CUDA 10.1        : pip install -U monk-cuda101
• CUDA 10.2        : pip install -U monk-cuda102
• CPU (+Mac-OS) : pip install -U monk-cpu
• Google Colab   : pip install -U monk-colab
• Kaggle              : pip install -U monk-kaggle

For More Installation instructions visit: Link

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Study Roadmaps

• Getting started with Monk
  • Essential notebooks to use all the monk's features
• Image Processing and Deep Learning
  • Learn both the basic and advanced concepts of image processing and deep learning
• Transfer Learning
  • Understand transfer learning in the AI field
• Image classification zoo
  • A list of 50+ real world image classification examples

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Documentation

• List of available models, layers, blocks, optimizers

• 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

• Features and Functions (In development):

  • Introduction
  • Setup
  • Quick Mode
  • Update Mode
  • Expert Mode
  • Hyper Parameter Analyser
  • Compare Experiments
  • Resume Training

• Complete Latest Docs (In Progress)

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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

General

• Functional Documentation
• Tackle Multiple versions of libraries
• Add unit-testing
• Contribution guidelines
• Python pip packaging support

Backend Support

• Tensorflow 2.0 provision support with v1
• Tensorflow 2.0 complete
• Chainer

External Libraries

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

Connect with the project contributors

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.