Monk Classification - CPU - 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
- 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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
monk_gluon_cpu_test-0.0.11.tar.gz
(238.4 kB
view hashes)
Built Distribution
Close
Hashes for monk_gluon_cpu_test-0.0.11.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 171b76451ea1d3750db2069ab799f03716eabb8885e84303aa26b586d8c5580c |
|
MD5 | b04a6df5f8bc79628c51897ebb31463f |
|
BLAKE2b-256 | d90abf241d418bec2c52071bbd870bd2dec555bca4a2babbd7b2c706a0e6b41c |
Close
Hashes for monk_gluon_cpu_test-0.0.11-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fbd67c5325a2c633c1bff21111ef346d997b80461e1ec326f09e3fd6cc52c4f7 |
|
MD5 | 8ae4b84137765eba1b8aab08d5ddc525 |
|
BLAKE2b-256 | 987c8221d374e0d7970db1c637ea5cd7023c3b88142e9f414dd6fe6836b1a4aa |