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Monk Classification Library - Cuda90 - backends - pytorch

Project description

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Website

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

Version     Build_Status

Table of Contents




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




Documentation




TODO-2020

TODO-2020 - Features

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

TODO-2020 - General

  • [ ] Incorporate pep coding standards
  • [x] Functional Documentation
  • [x] Tackle Multiple versions of libraries
  • [x] 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.

Project details


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