[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71185":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":17,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":18,"rankGlobal":10,"rankLanguage":10,"license":19,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":20,"hasPages":22,"topics":23,"createdAt":10,"pushedAt":10,"updatedAt":24,"readmeContent":25,"aiSummary":26,"trendingCount":16,"starSnapshotCount":16,"syncStatus":17,"lastSyncTime":27,"discoverSource":28},71185,"AugLy","facebookresearch\u002FAugLy","facebookresearch","A data augmentations library for audio, image, text, and video.","https:\u002F\u002Fai.facebook.com\u002Fblog\u002Faugly-a-new-data-augmentation-library-to-help-build-more-robust-ai-models\u002F",null,"Python",5084,312,79,22,0,2,64.69,"Other",false,"main",true,[],"2026-06-12 04:00:59","\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Ffacebookresearch\u002FAugLy\u002Fmain\u002Flogo.svg\" alt=\"logo\" width=\"70%\" \u002F>\n\u003C\u002Fp>\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FAugLy\u002Factions\">\n    \u003Cimg alt=\"Github Actions\" src=\"https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FAugLy\u002Factions\u002Fworkflows\u002Ftest_python.yml\u002Fbadge.svg\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fpypi.python.org\u002Fpypi\u002Faugly\u002F\">\n    \u003Cimg alt=\"PyPI downloads per month\" src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Faugly.svg\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fpypi.python.org\u002Fpypi\u002Faugly\">\n    \u003Cimg alt=\"PyPI Version\" src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Faugly\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffacebookresearch\u002FAugLy\u002Fblob\u002Fmain\u002Fexamples\u002FAugLy_image.ipynb\">\n    \u003Cimg alt=\"Image Colab notebook\" src=\"https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.5014032\">\n    \u003Cimg  alt=\"DOI\" src=\"https:\u002F\u002Fzenodo.org\u002Fbadge\u002FDOI\u002F10.5281\u002Fzenodo.5014032.svg\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FAugLy\u002Fblob\u002Fmain\u002FCONTRIBUTING.md\">\n    \u003Cimg alt=\"PRs Welcome\" src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg\"\u002F>\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n----------------------\n\nAugLy is a data augmentations library that currently supports four modalities ([audio](augly\u002Faudio), [image](augly\u002Fimage), [text](augly\u002Ftext) & [video](augly\u002Fvideo)) and over 100 augmentations. Each modality’s augmentations are contained within its own sub-library. These sub-libraries include both function-based and class-based transforms, composition operators, and have the option to provide metadata about the transform applied, including its intensity.\n\nAugLy is a great library to utilize for augmenting your data in model training, or to evaluate the robustness gaps of your model! We designed AugLy to include many specific data augmentations that users perform in real life on internet platforms like Facebook's -- for example making an image into a meme, overlaying text\u002Femojis on images\u002Fvideos, reposting a screenshot from social media. While AugLy contains more generic data augmentations as well, it will be particularly useful to you if you're working on a problem like copy detection, hate speech detection, or copyright infringement where these \"internet user\" types of data augmentations are prevalent.\n\n![Visual](https:\u002F\u002Fraw.githubusercontent.com\u002Ffacebookresearch\u002FAugLy\u002Fmain\u002Fimage_visual.png)\n\nTo see more examples of augmentations, open the Colab notebooks in the README for each modality! (e.g. image [README](augly\u002Fimage) & [Colab](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffacebookresearch\u002FAugLy\u002Fblob\u002Fmain\u002Fexamples\u002FAugLy_image.ipynb))\n\nThe library is Python-based and requires at least Python 3.6, as we use dataclasses.\n\n## Authors\n\n[**Joanna Bitton**](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fjoanna-bitton\u002F) — Software Engineer at Meta AI\n\n[**Zoe Papakipos**](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fzoe-papakipos-8637155b\u002F) — Software Engineer at Meta AI\n\n## Installation\n\n`AugLy` is a Python 3.6+ library. It can be installed with:\n\n```bash\npip install augly[all]\n```\n\nIf you want to only install the dependencies needed for one sub-library e.g. audio, you can install like so:\n\n```bash\npip install augly[audio]\n```\n\nOr clone AugLy if you want to be able to run our unit tests, contribute a pull request, etc:\n```bash\ngit clone git@github.com:facebookresearch\u002FAugLy.git && cd AugLy\n[Optional, but recommended] conda create -n augly && conda activate augly && conda install pip\npip install -e .[all]\n```\n\n**Backwards compatibility note**: In versions `augly\u003C=0.2.1` we did not separate the dependencies by modality. For those versions to install most dependencies you could use `pip install augly`, and if you want to use the audio or video modalities you would install with `pip install augly[av]`.\n\nIn some environments, `pip` doesn't install `python-magic` as expected. In that case, you will need to additionally run:\n```bash\nconda install -c conda-forge python-magic\n```\n\nOr if you aren't using conda:\n```bash\nsudo apt-get install python3-magic\n```\n\n## Documentation\n\nCheck out our [documentation](https:\u002F\u002Faugly.readthedocs.io\u002Fen\u002Flatest\u002F) on ReadtheDocs!\n\nFor more details about how to use each sub-library, how to run the tests, and links to colab notebooks with runnable examples, please see the READMEs in each respective directory ([audio](augly\u002Faudio\u002F), [image](augly\u002Fimage\u002F), [text](augly\u002Ftext\u002F), & [video](augly\u002Fvideo\u002F)).\n\n## Assets\n\nWe provide various media assets to use with some of our augmentations. These assets include:\n1. [Emojis](augly\u002Fassets\u002Ftwemojis\u002F) ([Twemoji](https:\u002F\u002Ftwemoji.twitter.com\u002F)) - Copyright 2020 Twitter, Inc and other contributors. Code licensed under the MIT License. Graphics licensed under CC-BY 4.0.\n2. [Fonts](augly\u002Fassets\u002Ffonts\u002F) ([Noto fonts](https:\u002F\u002Fwww.google.com\u002Fget\u002Fnoto\u002F)) - Noto is a trademark of Google Inc. Noto fonts are open source. All Noto fonts are published under the SIL Open Font License, Version 1.1.\n3. [Screenshot Templates](augly\u002Fassets\u002Fscreenshot_templates\u002F) - Images created by a designer at Facebook specifically to use with AugLy. You can use these with the `overlay_onto_screenshot` augmentation in both the image and video libraries to make it look like your source image\u002Fvideo was screenshotted in a social media feed similar to Facebook or Instagram.\n\n## Links\n\n1. Facebook AI blog post: https:\u002F\u002Fai.facebook.com\u002Fblog\u002Faugly-a-new-data-augmentation-library-to-help-build-more-robust-ai-models\u002F\n2. PyPi package: https:\u002F\u002Fpypi.org\u002Fproject\u002Faugly\u002F\n3. Arxiv paper: https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.06494\n4. Examples: https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FAugLy\u002Ftree\u002Fmain\u002Fexamples\n\n## Uses of AugLy in the wild\n1. [Image Similarity Challenge](https:\u002F\u002Fai.facebook.com\u002Fblog\u002Fthe-image-similarity-challenge-and-data-set-for-detecting-image-manipulation) - a NeurIPS 2021 competition run by Facebook AI with $200k in prizes, currently open for sign ups; also produced the DISC21 dataset, which will be made publicly available after the challenge concludes!\n2. [DeepFake Detection Challenge](https:\u002F\u002Fai.facebook.com\u002Fdatasets\u002Fdfdc\u002F) - a Kaggle competition run by Facebook AI in 2020 with $1 million in prizes; also produced the [DFDC dataset](https:\u002F\u002Fdfdc.ai)\n3. [SimSearchNet](https:\u002F\u002Fai.facebook.com\u002Fblog\u002Fusing-ai-to-detect-covid-19-misinformation-and-exploitative-content\u002F) - a near-duplicate detection model developed at Facebook AI to identify infringing content on our platforms\n\n## Citation\n\nIf you use AugLy in your work, please cite our [Arxiv paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.06494) using the citation below:\n\n```bibtex\n@misc{papakipos2022augly,\n  author        = {Zoe Papakipos and Joanna Bitton},\n  title         = {AugLy: Data Augmentations for Robustness},\n  year          = {2022},\n  eprint        = {2201.06494},\n  archivePrefix = {arXiv},\n  primaryClass  = {cs.AI}}\n}\n```\n\n## License\n\nAugLy is MIT licensed, as found in the [LICENSE](LICENSE) file. Please note that some of the dependencies AugLy uses may be licensed under different terms.\n","AugLy是一个用于音频、图像、文本和视频的数据增强库。它支持超过100种数据增强方法，每种模态的增强功能都包含在其子库中，提供函数式和类式转换，并可选择提供关于所应用变换及其强度的元数据。该库特别适用于需要考虑互联网用户行为（如制作表情包、在图片或视频上叠加文字\u002F表情符号等）的数据集扩增场景，尤其适合于处理诸如内容复制检测、仇恨言论识别或版权侵权等问题。AugLy基于Python开发，要求至少使用Python 3.6版本。","2026-06-11 03:36:28","high_star"]