Metadata-Version: 2.1
Name: icevision
Version: 0.1.1rc2
Summary: An Agnostic Computer Vision Framework.
Home-page: https://github.com/airctic/icevision/tree/master/
Author: Lucas Goulart Vazquez, Farid Hassainia, and Contributors
Author-email: icevision@airctic.com
License: Apache Software License 2.0
Description: <div align="center">
          <img src="images/icevision-logo-slogan.png" alt="logo" width="400px" style="display: block; margin-left: auto; margin-right: auto"/>
          <h2><b>An Agnostic Object Detection Framework</b></h2>
        </div>
        
        * * * * *
        >**Note: "We Need Your Help"**
            If you find this work useful, please let other people know by **starring** it,
            and sharing it. 
            Thank you!
            
        <div align="center">
            
        [![tests](https://github.com/airctic/icevision/workflows/tests/badge.svg?event=push)](https://github.com/airctic/icevision/actions?query=workflow%3Atests)
        [![docs](https://github.com/airctic/icevision/workflows/docs/badge.svg)](https://airctic.github.io/icevision/index.html)
        [![codecov](https://codecov.io/gh/airctic/icevision/branch/master/graph/badge.svg)](https://codecov.io/gh/airctic/icevision)
        [![black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
        [![license](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/airctic/icevision/blob/master/LICENSE)  
        
        [![Join Users Forum](https://withspectrum.github.io/badge/badge.svg)](https://spectrum.chat/mantis)
        
        </div>
        
        
        * * * * *
        
        ![image](images/icevision-end-to-end-training.gif)
        
        <!-- Not included in docs - start -->
        ## **Contributors**
        
        [![](https://sourcerer.io/fame/lgvaz/airctic/icevision/images/0)](https://sourcerer.io/fame/lgvaz/airctic/icevision/links/0)[![](https://sourcerer.io/fame/lgvaz/airctic/icevision/images/1)](https://sourcerer.io/fame/lgvaz/airctic/icevision/links/1)[![](https://sourcerer.io/fame/lgvaz/airctic/icevision/images/2)](https://sourcerer.io/fame/lgvaz/airctic/icevision/links/2)[![](https://sourcerer.io/fame/lgvaz/airctic/icevision/images/3)](https://sourcerer.io/fame/lgvaz/airctic/icevision/links/3)[![](https://sourcerer.io/fame/lgvaz/airctic/icevision/images/4)](https://sourcerer.io/fame/lgvaz/airctic/icevision/links/4)[![](https://sourcerer.io/fame/lgvaz/airctic/icevision/images/5)](https://sourcerer.io/fame/lgvaz/airctic/icevision/links/5)[![](https://sourcerer.io/fame/lgvaz/airctic/icevision/images/6)](https://sourcerer.io/fame/lgvaz/airctic/icevision/links/6)[![](https://sourcerer.io/fame/lgvaz/airctic/icevision/images/7)](https://sourcerer.io/fame/lgvaz/airctic/icevision/links/7)
        
        ## Installation
        
        ```bash
        pip install icevision[all]
        pip install pycocotools@https://github.com/lgvaz/cocoapi/archive/master.zip#subdirectory=PythonAPI&egg=pycocotools-2.0
        pip install omegaconf effdet@https://github.com/rwightman/efficientdet-pytorch/archive/master.zip#egg=effdet-0.1.4
        ```
        
        For more installation options, check our [docs](https://airctic.github.io/icevision/install/).
        
        **Important:** We currently only support Linux/MacOS.
        <!-- Not included in docs - end -->
        
        
        ## Quick Example: How to train the **PETS Dataset**
        [**Source Code**](https://airctic.github.io/icevision/examples/training/)
        ![image](images/icevision-readme.png)
        
        
        
        ## The Problem We Are Solving
        
        -   Object dectection datasets come in different sizes and most
            impotantly have different annotations formats ranging from the
            stanndard formarts such COCO and VOC to more self-tailored formats
        -   When new object detection models are released with some source code,
            the latter is very often written in non-portable way: The source
            code is difficult to use for other datasets because of some
            hard-coded parts coupled with self developed tweaks
        -   Both researchers and DL coders have to deploy a lot of effort to use
            many SOTA models for their own use-cases and/or to craft an enhanced
            model based on those already published
        
        ## Our Solution
        
        IceVision library provides some elegant solutions in those 2
        fundamental components:
        
        **1- A Unified Data API**
        
        Out of the box, we offer several annotation parsers that translates
        different annotation formats into a very flexibe parser:
        
        * By default, we offer differents standard format parsers such as COCO
          and VOC.
        * We host a community curated parsers where community contributors
          publish their own parsers to be shared, and therefore save time and
          energy in creating similar parsers over and over.
        * We provide some intuitive tutorials that walk you through the steps
          of creating your own parser. Please, consider sharing it with the
          whole community.
        
        **2- A Universal Adapter to different DL Libraries**
        
        * IceVision provides a universal adapter that allows you to hook up
          your dataset to the DL library of your choice (fastai, Pytorch
          Lightning and Pytorch), and train your model using a familiar API.
        * Our library allows you to choose one of the public implementations
          of a given model, plug it in icevision model adapter, and
          seamlessly train your model.
        * As a bonus, our library even allows to experiment with another DL
          library. Our tutorials have several examples showing you how to
          train a given model using both fastai and Pytorch Lightning
          libraries side by side.
        
        
        ## Happy Learning!
        If you need any assistance, feel free to:
        
        [Join our Users Forum](https://spectrum.chat/mantis)
        
        [Join our Devs Forum](https://discord.gg/QxHctJF)
        
Keywords: computer-vision,object-detection,ai,deep-learning,dl,pytorch,fastai,pytorch-lightning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: pytorch-lightning
Provides-Extra: fastai
Provides-Extra: inference
Provides-Extra: all
