Monk Classification Library - Cuda92 - backends - pytorch, keras, gluon
Project description
monk_v1
![](https://pypi-camo.freetls.fastly.net/945b4070f44754d06765a4a85f9e7dd5b361beed/68747470733a2f2f746f6b65692e72732f62312f6769746875622f54657373656c6c6174652d496d6167696e672f6d6f6e6b5f76313f63617465676f72793d66696c6573)
Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.
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
- Getting started with Monk
- Python sample examples
- Image Processing and Deep Learning
- Transfer Learning
- Image classification zoo
Documentation
-
Functional Documentation (Will be merged with Latest docs soon)
-
Features and Functions (In development):
-
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.
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