Monk Classification Library - Cuda101 - backends - mxnet-gluon
Project description
monk_v1

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
- [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.
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