[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71172":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":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":24,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":46,"readmeContent":47,"aiSummary":48,"trendingCount":15,"starSnapshotCount":15,"syncStatus":49,"lastSyncTime":50,"discoverSource":51},71172,"sahi","obss\u002Fsahi","obss","Framework agnostic sliced\u002Ftiled inference + interactive ui + error analysis plots","https:\u002F\u002Fobss.github.io\u002Fsahi\u002F",null,"Python",5344,751,48,0,7,20,70,21,95.63,"MIT License",false,"main",true,[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"coco","computer-vision","deep-learning","explainable-ai","fiftyone","hacktoberfest","huggingface","instance-segmentation","large-image","machine-learning","mmdetection","object-detection","oriented-object-detection","python","pytorch","remote-sensing","satellite","small-object-detection","tiling","yolo11","2026-06-12 04:00:59","\u003Cdiv align=\"center\">\n\u003Ch1>\n SAHI: Slicing Aided Hyper Inference\n\u003C\u002Fh1>\n\n\u003Ch4>\n A lightweight vision library for performing large scale object detection & instance segmentation\n\u003C\u002Fh4>\n\n\u003Ch4>\n \u003Cimg width=\"700\" alt=\"teaser\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fobss\u002Fsahi\u002Fmain\u002Fresources\u002Fsahi-sliced-inference-overview.avif\">\n\u003C\u002Fh4>\n\n\u003C!-- Downloads & Version -->\n\u003Cdiv>\n \u003Ca href=\"https:\u002F\u002Fpepy.tech\u002Fproject\u002Fsahi\">\u003Cimg 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src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDeepWiki-obss%2Fsahi-blue.svg?logo=data:image\u002Fpng;base64,iVBORw0KGgoAAAANSUhEUgAAACwAAAAyCAYAAAAnWDnqAAAAAXNSR0IArs4c6QAAA05JREFUaEPtmUtyEzEQhtWTQyQLHNak2AB7ZnyXZMEjXMGeK\u002FAIi+QuHrMnbChYY7MIh8g01fJoopFb0uhhEqqcbWTp06\u002Fuv1saEDv4O3n3dV60RfP947Mm9\u002FSQc0ICFQgzfc4CYZoTPAswgSJCCUJUnAAoRHOAUOcATwbmVLWdGoH\u002F\u002FPB8mnKqScAhsD0kYP3j\u002FYt5LPQe2KvcXmGvRHcDnpxfL2zOYJ1mFwrryWTz0advv1Ut4CJgf5uhDuDj5eUcAUoahrdY\u002F56ebRWeraTjMt\u002F00Sh3UDtjgHtQNHwcRGOC98BJEAEymycmYcWwOprTgcB6VZ5JK5TAJ+fXGLBm3FDAmn6oPPjR4rKCAoJCal2eAiQp2x0vxTPB3ALO2CRkwmDy5WohzBDwSEFKRwPbknEggCPB\u002FimwrycgxX2NzoMCHhPkDwqYMr9tRcP5qNrMZHkVnOjRMWwLCcr8ohBVb1OMjxLwGCvjTikrsBOiA6fNyCrm8V1rP93iVPpwaE+gO0SsWmPiXB+jikdf6SizrT5qKasx5j8ABbHpFTx+vFXp9EnYQmLx02h1QTTrl6eDqxLnGjporxl3NL3agEvXdT0WmEost648sQOYAeJS9Q7bfUVoMGnjo4AZdUMQku50McDcMWcBPvr0SzbTAFDfvJqwLzgxwATnCgnp4wDl6Aa+Ax283gghmj+vj7feE2KBBRMW3FzOpLOADl0Isb5587h\u002FU4gGvkt5v60Z1VLG8BhYjbzRwyQZemwAd6cCR5\u002FXFWLYZRIMpX39AR0tjaGGiGzLVyhse5C9RKC6ai42ppWPKiBagOvaYk8lO7DajerabOZP46Lby5wKjw1HCRx7p9sVMOWGzb\u002FvA1hwiWc6jm3MvQDTogQkiqIhJV0nBQBTU+3okKCFDy9WwferkHjtxib7t3xIUQtHxnIwtx4mpg26\u002FHfwVNVDb4oI9RHmx5WGelRVlrtiw43zboCLaxv46AZeB3IlTkwouebTr1y2NjSpHz68WNFjHvupy3q8TFn3Hos2IAk4Ju5dCo8B3wP7VPr\u002FFGaKiG+T+v+TQqIrOqMTL1VdWV1DdmcbO8KXBz6esmYWYKPwDL5b5FA1a0hwapHiom0r\u002FcKaoqr+27\u002FXcrS5UwSMbQAAAABJRU5ErkJggg==\" alt=\"DeepWiki\">\u003C\u002Fa>\n \u003Ca href=\"https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Ffcakyon\u002Fsahi-yolox\">\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fobss\u002Fsahi\u002Fmain\u002Fresources\u002Fhf_spaces_badge.svg\" alt=\"HuggingFace Spaces\">\u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\u003C\u002Fdiv>\n\n## \u003Cdiv align=\"center\">Overview\u003C\u002Fdiv>\n\nSAHI helps developers overcome real-world challenges in object detection by\nenabling **sliced inference** for detecting small objects in large images. It\nsupports various popular detection models and provides easy-to-use APIs.\n\n\u003Cdiv align=\"center\">\n\n🌐 [English](README.md) | 🇨🇳 [简体中文](docs\u002Fzh\u002FREADME.md)\n\n\u003C\u002Fdiv>\n\n| Command | Description |\n| ----------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| [predict](https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdocs\u002Fcli.md#predict-command-usage) | Perform sliced\u002Fstandard video\u002Fimage prediction using any [ultralytics](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fultralytics) \u002F [mmdet](https:\u002F\u002Fgithub.com\u002Fopen-mmlab\u002Fmmdetection) \u002F [huggingface](https:\u002F\u002Fhuggingface.co\u002Fmodels?pipeline_tag=object-detection&sort=downloads) \u002F [torchvision](https:\u002F\u002Fpytorch.org\u002Fvision\u002Fstable\u002Fmodels.html#object-detection) model — see [CLI guide](docs\u002Fcli.md#predict-command-usage) |\n| [predict-fiftyone](https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdocs\u002Fcli.md#predict-fiftyone-command-usage) | Perform sliced\u002Fstandard prediction using any supported model and explore results in [fiftyone app](https:\u002F\u002Fgithub.com\u002Fvoxel51\u002Ffiftyone) — [learn more](docs\u002Ffiftyone.md) |\n| [coco slice](https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdocs\u002Fcli.md#coco-slice-command-usage) | Automatically slice COCO annotation and image files — see [slicing utilities](docs\u002Fslicing.md) |\n| [coco fiftyone](https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdocs\u002Fcli.md#coco-fiftyone-command-usage) | Explore multiple prediction results on your COCO dataset with [fiftyone ui](https:\u002F\u002Fgithub.com\u002Fvoxel51\u002Ffiftyone) ordered by number of misdetections |\n| [coco evaluate](https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdocs\u002Fcli.md#coco-evaluate-command-usage) | Evaluate classwise COCO AP and AR for given predictions and ground truth — check [COCO utilities](docs\u002Fcoco.md) |\n| [coco analyse](https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdocs\u002Fcli.md#coco-analyse-command-usage) | Calculate and export many error analysis plots — see the [complete guide](docs\u002FREADME.md) |\n| [coco yolo](https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdocs\u002Fcli.md#coco-yolo-command-usage) | Automatically convert any COCO dataset to [ultralytics](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fultralytics) format |\n\n### Approved by the Community\n\n[📜 List of publications that cite SAHI (currently 600+)](https:\u002F\u002Fscholar.google.com\u002Fscholar?hl=en&as_sdt=2005&sciodt=0,5&cites=14065474760484865747&scipsc=&q=&scisbd=1)\n\n[🏆 List of competition winners that used SAHI](https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fdiscussions\u002F688)\n\n### Approved by AI Tools\n\nSAHI's documentation is\n[indexed in Context7 MCP](https:\u002F\u002Fcontext7.com\u002Fobss\u002Fsahi), providing AI coding\nassistants with up-to-date, version-specific code examples and API references.\nWe also provide an [llms.txt](https:\u002F\u002Fcontext7.com\u002Fobss\u002Fsahi\u002Fllms.txt) file\nfollowing the emerging standard for AI-readable documentation. To integrate SAHI\ndocs with your AI development workflow, check out the\n[Context7 MCP installation guide](https:\u002F\u002Fgithub.com\u002Fupstash\u002Fcontext7#%EF%B8%8F-installation).\n\n## \u003Cdiv align=\"center\">Installation\u003C\u002Fdiv>\n\n### Basic Installation\n\n```bash\npip install sahi\n```\n\n\u003Cdetails closed>\n\u003Csummary>\n\u003Cbig>\u003Cb>Detailed Installation (Click to open)\u003C\u002Fb>\u003C\u002Fbig>\n\u003C\u002Fsummary>\n\n- Install your desired version of pytorch and torchvision:\n\n```console\npip install torch==2.7.0 torchvision==0.22.0 --index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu126\n```\n\n(torch 2.1.2 is required for mmdet support):\n\n```console\npip install torch==2.1.2 torchvision==0.16.2 --index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu121\n```\n\n- Install your desired detection framework (ultralytics):\n\n```console\npip install ultralytics>=8.3.161\n```\n\n- Install your desired detection framework (huggingface):\n\n```console\npip install transformers>=4.49.0 timm\n```\n\n- Install your desired detection framework (yolov5):\n\n```console\npip install yolov5==7.0.14 sahi==0.11.21\n```\n\n- Install your desired detection framework (mmdet):\n\n```console\npip install mim\nmim install mmdet==3.3.0\n```\n\n- Install your desired detection framework (roboflow):\n\n```console\npip install inference>=0.51.5 rfdetr>=1.6.2\n```\n\n\u003C\u002Fdetails>\n\n## \u003Cdiv align=\"center\">Quick Start\u003C\u002Fdiv>\n\n### Learning Resources\n\n| Resource | Type |\n| --------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- |\n| [Introduction to SAHI](https:\u002F\u002Fmedium.com\u002Fcodable\u002Fsahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80) | Blog Post |\n| [2025 Video Tutorial](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ILqMBah5ZvI) ⭐ | Video |\n| [Official Paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9897990) (ICIP 2022 oral) | Paper |\n| [Pretrained Weights & ICIP 2022 Paper Files](https:\u002F\u002Fgithub.com\u002Ffcakyon\u002Fsmall-object-detection-benchmark) | Benchmark |\n| [Visualizing and Evaluating SAHI Predictions with FiftyOne](https:\u002F\u002Fvoxel51.com\u002Fblog\u002Fhow-to-detect-small-objects\u002F) | Blog Post |\n| [Exploring SAHI – learnopencv.com](https:\u002F\u002Flearnopencv.com\u002Fslicing-aided-hyper-inference\u002F) | Article |\n| [Slicing Aided Hyper Inference Explained by Encord](https:\u002F\u002Fencord.com\u002Fblog\u002Fslicing-aided-hyper-inference-explained\u002F) | Article |\n| [Video Tutorial: SAHI for Small Object Detection](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UuOJKxn-M8&t=270s) | Video |\n| [Satellite Object Detection](https:\u002F\u002Fblog.ml6.eu\u002Fhow-to-detect-small-objects-in-very-large-images-70234bab0f98) | Blog Post |\n| [COCO Dataset Conversion](https:\u002F\u002Fmedium.com\u002Fcodable\u002Fconvert-any-dataset-to-coco-object-detection-format-with-sahi-95349e1fe2b7) | Blog Post |\n| [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fremekkinas\u002Fsahi-slicing-aided-hyper-inference-yv5-and-yx) | Notebook |\n| [Error Analysis Plots & Evaluation](https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fdiscussions\u002F622) ⭐ | Discussion |\n| [Interactive Result Visualization and Inspection](https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fdiscussions\u002F624) ⭐ | Discussion |\n| [Video Inference Support](https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fdiscussions\u002F626) | Discussion |\n| [Slicing Operation Notebook](demo\u002Fslicing.ipynb) | Notebook |\n| [Complete Documentation](docs\u002FREADME.md) | Docs |\n\n### Notebooks & Demos\n\n| Framework | Notebook | Demo |\n| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| YOLO12 | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdemo\u002Finference_for_ultralytics.ipynb) | — |\n| YOLO11 | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdemo\u002Finference_for_ultralytics.ipynb) | — |\n| YOLO11-OBB | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdemo\u002Finference_for_ultralytics.ipynb) | — |\n| Roboflow \u002F RF-DETR | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdemo\u002Finference_for_roboflow.ipynb) | — |\n| RT-DETR v2 | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdemo\u002Finference_for_huggingface.ipynb) | — |\n| RT-DETR | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdemo\u002Finference_for_rtdetr.ipynb) | — |\n| HuggingFace | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdemo\u002Finference_for_huggingface.ipynb) | — |\n| YOLOv5 | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdemo\u002Finference_for_yolov5.ipynb) | — |\n| MMDetection | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdemo\u002Finference_for_mmdetection.ipynb) | — |\n| TorchVision | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fobss\u002Fsahi\u002Fblob\u002Fmain\u002Fdemo\u002Finference_for_torchvision.ipynb) | — |\n| YOLOX | — | [![HuggingFace Spaces](https:\u002F\u002Fraw.githubusercontent.com\u002Fobss\u002Fsahi\u002Fmain\u002Fresources\u002Fhf_spaces_badge.svg)](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Ffcakyon\u002Fsahi-yolox) |\n\n\u003Ca href=\"https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Ffcakyon\u002Fsahi-yolox\">\u003Cimg width=\"600\" src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F34196005\u002F144092739-c1d9bade-a128-4346-947f-424ce00e5c4f.gif\" alt=\"sahi-yolox\">\u003C\u002Fa>\n\n### Framework Agnostic Sliced\u002FStandard Prediction\n\n\u003Cimg width=\"700\" alt=\"sahi-predict\" src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F34196005\u002F149310540-e32f504c-6c9e-4691-8afd-59f3a1a457f0.gif\">\n\nFind detailed info on using `sahi predict` command in the\n[CLI documentation](docs\u002Fcli.md#predict-command-usage) and explore the\n[prediction API](docs\u002Fpredict.md) for advanced usage.\n\nFind detailed info on video inference at\n[video inference tutorial](https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fdiscussions\u002F626).\n\n### Error Analysis Plots & Evaluation\n\n\u003Cimg width=\"700\" alt=\"sahi-analyse\" src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F34196005\u002F149537858-22b2e274-04e8-4e10-8139-6bdcea32feab.gif\">\n\nFind detailed info at\n[Error Analysis Plots & Evaluation](https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fdiscussions\u002F622).\n\n### Interactive Visualization & Inspection\n\n\u003Cimg width=\"700\" alt=\"sahi-fiftyone\" src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F34196005\u002F149321540-e6dd5f3-36dc-4267-8574-a985dd0c6578.gif\">\n\nExplore [FiftyOne integration](docs\u002Ffiftyone.md) for interactive visualization\nand inspection.\n\n### Other Utilities\n\nCheck the [comprehensive COCO utilities guide](docs\u002Fcoco.md) for YOLO\nconversion, dataset slicing, subsampling, filtering, merging, and splitting\noperations. Learn more about the [slicing utilities](docs\u002Fslicing.md) for\ndetailed control over image and dataset slicing parameters.\n\n## \u003Cdiv align=\"center\">Citation\u003C\u002Fdiv>\n\nIf you use this package in your work, please cite as:\n\n```bibtex\n@article{akyon2022sahi,\n title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection},\n author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin},\n journal={2022 IEEE International Conference on Image Processing (ICIP)},\n doi={10.1109\u002FICIP46576.2022.9897990},\n pages={966-970},\n year={2022}\n}\n```\n\n```bibtex\n@software{obss2021sahi,\n author = {Akyon, Fatih Cagatay and Cengiz, Cemil and Altinuc, Sinan Onur and Cavusoglu, Devrim and Sahin, Kadir and Eryuksel, Ogulcan},\n title = {{SAHI: A lightweight vision library for performing large scale object detection and instance segmentation}},\n month = nov,\n year = 2021,\n publisher = {Zenodo},\n doi = {10.5281\u002Fzenodo.5718950},\n url = {https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.5718950}\n}\n```\n\n## \u003Cdiv align=\"center\">Contributing\u003C\u002Fdiv>\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md)\nto get started. Thank you 🙏 to all our contributors!\n\n\u003Cp align=\"center\">\n \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fobss\u002Fsahi\u002Fgraphs\u002Fcontributors\">\n \u003Cimg src=\"https:\u002F\u002Fcontrib.rocks\u002Fimage?repo=obss\u002Fsahi\" \u002F>\n \u003C\u002Fa>\n\u003C\u002Fp>\n","SAHI 是一个用于执行大规模物体检测和实例分割的轻量级视觉库。该项目支持框架无关的切片\u002F平铺推理，通过将大图像分割成小块来提高模型在处理大型图像时的性能，并能够合并这些小块的结果以获得最终预测。此外，SAHI 还提供了交互式用户界面以及错误分析图，帮助开发者更好地理解和调试模型。它非常适合需要对高分辨率图片进行精确对象识别的应用场景，如遥感、卫星图像分析及医学影像处理等。项目采用 Python 编写，遵循 MIT 许可证发布。",2,"2026-06-11 03:36:25","high_star"]