monk-gluon-cpu

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

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Monk - A computer vision toolkit for everyone Tweet

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Why use Monk

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


Create real-world Image Classification applications

Medical Domain Fashion Domain Autonomous Vehicles Domain
Agriculture Domain Wildlife Domain Retail Domain
Satellite Domain Healthcare Domain Activity Analysis Domain

...... 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 - 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();



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




Study Roadmaps




Documentation




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 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.

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