Package: autogluon.vision
AutoML for Image, Text, Time Series, and Tabular DataInstall Instructions| Documentation (Stable|Latest)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# First install package from terminal:# pip install -U pip# pip install -U setuptools wheel# pip install autogluon  # autogluon==0.6.1fromautogluon.tabularimportTabularDataset,TabularPredictortrain_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 120sleaderboard=predictor.leaderboard(test_data)AutoGluon TaskQuickstartAPITabularPredictorTextPredictorImagePredictorObjectDetectorMultiModalPredictorTimeSeriesPredictorResourcesSee theAutoGluon Websitefordocumentationand instructions on:Installing AutoGluonLearning with tabular dataTips to maximize accuracy(ifbenchmarking, make sure to runfit()with argumentpresets='best_quality').Learning with text dataLearning with image dataLearning with time series dataRefer to theAutoGluon Roadmapfor details on upcoming features and releases.Scientific PublicationsAutoGluon-Tabular: Robust and Accurate AutoML for Structured Data(Arxiv, 2020)Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation(NeurIPS, 2020)Multimodal AutoML on Structured Tables with Text Fields(ICML AutoML Workshop, 2021)ArticlesAutoGluon for tabular data: 3 lines of code to achieve top 1% in Kaggle competitions(AWS Open Source Blog, Mar 2020)Accurate image classification in 3 lines of code with AutoGluon(Medium, Feb 2020)AutoGluon overview & example applications(Towards Data Science, Dec 2019)Hands-on TutorialsPractical Automated Machine Learning with Tabular, Text, and Image Data (KDD 2020)Train/Deploy AutoGluon in the CloudAutoGluon-Tabular on AWS MarketplaceAutoGluon-Tabular on Amazon SageMakerAutoGluon Deep Learning ContainersContributing to AutoGluonWe are actively accepting code contributions to the AutoGluon project. If you are interested in contributing to AutoGluon, please read theContributing Guideto get started.Citing AutoGluonIf 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."arXiv preprint arXiv:2003.06505 (2020).BibTeX entry:@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."Advances in Neural Information Processing Systems 33 (2020).BibTeX entry:@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."8th ICML Workshop on Automated Machine Learning (AutoML). 2021.BibTeX entry:@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 OptimizationAutoGluon's state-of-the-art tools for hyperparameter optimization, such as ASHA, Hyperband, Bayesian Optimization and BOHB have moved to the stand-alone packagesyne-tune.To learn more, checkout our paper"Model-based Asynchronous Hyperparameter and Neural Architecture Search"arXiv preprint arXiv:2003.10865 (2020).@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}}LicenseThis library is licensed under the Apache 2.0 License.