Best Practices, code samples, and documentation for Computer Vision.

artificial-intelligence azure computer-vision convolutional-neural-networks data-science deep-learning image-classification image-processing jupyter-notebook kubernetes machine-learning microsoft object-detection operationalization python similarity tutorial

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JS b01067535b benchmark + parameter_sweeper module (#55 ) * benchmark code * benchmark script update * update fastai code * benchmark code * benchmark script * flake8 req * flake8 req * train schedule * update * benchmarking nb * benchmark * benchmark stable * gitignore * experiments + test * delete benchmark.py * reformat msg in notebook * benchmark script * type error * fixes * fixes * fixes * lxml to env.yml		2019-03-21 12:25:46 -04:00
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image_classification	benchmark + parameter_sweeper module (#55 )	2019-03-21 12:25:46 -04:00
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.flake8	benchmark + parameter_sweeper module (#55 )	2019-03-21 12:25:46 -04:00
.gitignore	benchmark + parameter_sweeper module (#55 )	2019-03-21 12:25:46 -04:00
.pre-commit-config.yaml	Adding pre-commit package to yml, more rules to flake8 lint config and adding pre-commit config file	2019-03-13 14:21:15 -04:00
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README.md

Computer Vision Best Practices

This repository will provide examples and best practices for building Computer Vision systems, provided as Jupyter notebooks, and using PyTorch as Deep Learning library. Image classification will be covered first, followed by object detection and image similarity.

Planning etc documents

All feature planning is done via projects, milestones, and issues in this Github repository.

Getting Started

Instructions to get started are provided in the image classification README.md file.

Contributing

This project welcomes contributions and suggestions. Before contributing, please see our contribution guidelines.