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Website

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

Version     Build_Status

Documentation

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();

RoadMap

  • [] Incorporate pep coding standards
  • [] Functional Documentation
  • [] Tackle Multiple versions of pytorch, keras, gluon
  • [] Standardize folder structure for next feature additions - object detection, image segmentation
  • [] Add support for tensorflow-2.0
  • [] Add unit-testing

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.