Metadata-Version: 2.1
Name: autogluon.vision
Version: 0.6.1b20221125
Summary: AutoML for Image, Text, and Tabular Data
Home-page: https://github.com/awslabs/autogluon
Author: AutoGluon Community
License: Apache-2.0
Project-URL: Documentation, https://auto.gluon.ai
Project-URL: Bug Reports, https://github.com/awslabs/autogluon/issues
Project-URL: Source, https://github.com/awslabs/autogluon/
Project-URL: Contribute!, https://github.com/awslabs/autogluon/blob/master/CONTRIBUTING.md
Description: 
        
        <div align="left">
          <img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
        </div>
        
        ## AutoML for Image, Text, Time Series, and Tabular Data
        
        [![Latest Release](https://img.shields.io/github/v/release/awslabs/autogluon)](https://github.com/awslabs/autogluon/releases)
        [![Continuous Integration](https://github.com/awslabs/autogluon/actions/workflows/continuous_integration.yml/badge.svg)](https://github.com/awslabs/autogluon/actions/workflows/continuous_integration.yml)
        [![Platform Tests](https://github.com/awslabs/autogluon/actions/workflows/platform_tests-command.yml/badge.svg?event=schedule)](https://github.com/awslabs/autogluon/actions/workflows/platform_tests-command.yml)
        [![Python Versions](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue)](https://pypi.org/project/autogluon/)
        [![GitHub license](docs/static/apache2.svg)](./LICENSE)
        [![Downloads](https://pepy.tech/badge/autogluon/month)](https://pepy.tech/project/autogluon)
        [![Twitter](https://img.shields.io/twitter/follow/autogluon?style=social)](https://twitter.com/autogluon)
        
        [Install Instructions](https://auto.gluon.ai/stable/install.html) | Documentation ([Stable](https://auto.gluon.ai/stable/index.html) | [Latest](https://auto.gluon.ai/dev/index.html))
        
        AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.  With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, time series, and tabular data.
        
        ## Example
        
        ```python
        # First install package from terminal:
        # pip install -U pip
        # pip install -U setuptools wheel
        # pip install autogluon  # autogluon==0.6.0
        
        from autogluon.tabular import TabularDataset, TabularPredictor
        train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
        test_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')
        predictor = TabularPredictor(label='class').fit(train_data, time_limit=120)  # Fit models for 120s
        leaderboard = predictor.leaderboard(test_data)
        ```
        
        | AutoGluon Task      |                                                                                Quickstart                                                                                |                                                                                API                                                                                |
        |:--------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------:|
        | TabularPredictor    | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-quickstart.html) |                 [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#module-0)                 |
        | TextPredictor       | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/text_prediction/beginner.html)        |       [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#autogluon.text.TextPredictor)       |
        | ImagePredictor      | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/image_prediction/beginner.html)       |     [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#autogluon.vision.ImagePredictor)      |
        | ObjectDetector      | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/object_detection/beginner.html)       |     [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#autogluon.vision.ObjectDetector)      |
        | MultiModalPredictor | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/multimodal/index.html)            | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#autogluon.multimodal.MultiModalPredictor) |
        | TimeSeriesPredictor | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-quickstart.html)            | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#autogluon.timeseries.TimeSeriesPredictor) |
        
        ## Resources
        
        See the [AutoGluon Website](https://auto.gluon.ai/stable/index.html) for [documentation](https://auto.gluon.ai/stable/api/index.html) and instructions on:
        - [Installing AutoGluon](https://auto.gluon.ai/stable/index.html#installation)
        - [Learning with tabular data](https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-quickstart.html)
          - [Tips to maximize accuracy](https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-quickstart.html#maximizing-predictive-performance) (if **benchmarking**, make sure to run `fit()` with argument `presets='best_quality'`).  
        
        - [Learning with text data](https://auto.gluon.ai/stable/tutorials/text_prediction/beginner.html)
        - [Learning with image data](https://auto.gluon.ai/stable/tutorials/image_prediction/beginner.html)
        - [Learning with time series data](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-quickstart.html)
        
        Refer to the [AutoGluon Roadmap](https://github.com/awslabs/autogluon/blob/master/ROADMAP.md) for details on upcoming features and releases.
        
        ### Scientific Publications
        - [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://arxiv.org/pdf/2003.06505.pdf) (*Arxiv*, 2020)
        - [Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation](https://proceedings.neurips.cc/paper/2020/hash/62d75fb2e3075506e8837d8f55021ab1-Abstract.html) (*NeurIPS*, 2020)
        - [Multimodal AutoML on Structured Tables with Text Fields](https://openreview.net/pdf?id=OHAIVOOl7Vl) (*ICML AutoML Workshop*, 2021)
        
        ### Articles
        - [AutoGluon for tabular data: 3 lines of code to achieve top 1% in Kaggle competitions](https://aws.amazon.com/blogs/opensource/machine-learning-with-autogluon-an-open-source-automl-library/) (*AWS Open Source Blog*, Mar 2020)
        - [Accurate image classification in 3 lines of code with AutoGluon](https://medium.com/@zhanghang0704/image-classification-on-kaggle-using-autogluon-fc896e74d7e8) (*Medium*, Feb 2020)
        - [AutoGluon overview & example applications](https://towardsdatascience.com/autogluon-deep-learning-automl-5cdb4e2388ec?source=friends_link&sk=e3d17d06880ac714e47f07f39178fdf2) (*Towards Data Science*, Dec 2019)
        
        ### Hands-on Tutorials
        - [Practical Automated Machine Learning with Tabular, Text, and Image Data (KDD 2020)](https://jwmueller.github.io/KDD20-tutorial/)
        
        ### Train/Deploy AutoGluon in the Cloud
        - [AutoGluon-Tabular on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-n4zf5pmjt7ism)
        - [AutoGluon-Tabular on Amazon SageMaker](https://github.com/aws/amazon-sagemaker-examples/tree/master/advanced_functionality/autogluon-tabular-containers)
        - [AutoGluon Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#autogluon-training-containers)
        
        ## Contributing to AutoGluon
        
        We are actively accepting code contributions to the AutoGluon project. If you are interested in contributing to AutoGluon, please read the [Contributing Guide](https://github.com/awslabs/autogluon/blob/master/CONTRIBUTING.md) to get started.
        
        ## Citing AutoGluon
        
        If you use AutoGluon in a scientific publication, please cite the following paper:
        
        Erickson, Nick, et al. ["AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data."](https://arxiv.org/abs/2003.06505) arXiv preprint arXiv:2003.06505 (2020).
        
        BibTeX entry:
        
        ```bibtex
        @article{agtabular,
          title={AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data},
          author={Erickson, Nick and Mueller, Jonas and Shirkov, Alexander and Zhang, Hang and Larroy, Pedro and Li, Mu and Smola, Alexander},
          journal={arXiv preprint arXiv:2003.06505},
          year={2020}
        }
        ```
        
        If you are using AutoGluon Tabular's model distillation functionality, please cite the following paper:
        
        Fakoor, Rasool, et al. ["Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation."](https://proceedings.neurips.cc/paper/2020/hash/62d75fb2e3075506e8837d8f55021ab1-Abstract.html) Advances in Neural Information Processing Systems 33 (2020).
        
        BibTeX entry:
        
        ```bibtex
        @article{agtabulardistill,
          title={Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation},
          author={Fakoor, Rasool and Mueller, Jonas W and Erickson, Nick and Chaudhari, Pratik and Smola, Alexander J},
          journal={Advances in Neural Information Processing Systems},
          volume={33},
          year={2020}
        }
        ```
        
        If you use AutoGluon's multimodal text+tabular functionality in a scientific publication, please cite the following paper:
        
        Shi, Xingjian, et al. ["Multimodal AutoML on Structured Tables with Text Fields."](https://openreview.net/forum?id=OHAIVOOl7Vl) 8th ICML Workshop on Automated Machine Learning (AutoML). 2021.
        
        BibTeX entry:
        
        ```bibtex
        @inproceedings{agmultimodaltext,
          title={Multimodal AutoML on Structured Tables with Text Fields},
          author={Shi, Xingjian and Mueller, Jonas and Erickson, Nick and Li, Mu and Smola, Alex},
          booktitle={8th ICML Workshop on Automated Machine Learning (AutoML)},
          year={2021}
        }
        ```
        
        
        ## AutoGluon for Hyperparameter Optimization
        
        AutoGluon's state-of-the-art tools for hyperparameter optimization, such as ASHA, Hyperband, Bayesian Optimization and BOHB have moved to the stand-alone package [syne-tune](https://github.com/awslabs/syne-tune).
        
        To learn more, checkout our paper ["Model-based Asynchronous Hyperparameter and Neural Architecture Search"](https://arxiv.org/abs/2003.10865) arXiv preprint arXiv:2003.10865 (2020).
        
        ```bibtex
        @article{abohb,
          title={Model-based Asynchronous Hyperparameter and Neural Architecture Search},
          author={Klein, Aaron and Tiao, Louis and Lienart, Thibaut and Archambeau, Cedric and Seeger, Matthias},
          journal={arXiv preprint arXiv:2003.10865},
          year={2020}
        }
        ```
        
        
        ## License
        
        This library is licensed under the Apache 2.0 License.
        
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