SAHI: Slicing Aided Hyper Inference

A lightweight vision library for performing large scale object detection & instance segmentation

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##
Overview
SAHI helps developers overcome real-world challenges in object detection by enabling **sliced inference** for detecting small objects in large images. It supports various popular detection models and provides easy-to-use APIs.
🌐 [English](README.md) | 🇨đŸ‡ŗ [įŽ€äŊ“中文](docs/zh/README.md)
| Command | Description | | ----------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [predict](https://github.com/obss/sahi/blob/main/docs/cli.md#predict-command-usage) | Perform sliced/standard video/image prediction using any [ultralytics](https://github.com/ultralytics/ultralytics) / [mmdet](https://github.com/open-mmlab/mmdetection) / [huggingface](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads) / [torchvision](https://pytorch.org/vision/stable/models.html#object-detection) model — see [CLI guide](docs/cli.md#predict-command-usage) | | [predict-fiftyone](https://github.com/obss/sahi/blob/main/docs/cli.md#predict-fiftyone-command-usage) | Perform sliced/standard prediction using any supported model and explore results in [fiftyone app](https://github.com/voxel51/fiftyone) — [learn more](docs/fiftyone.md) | | [coco slice](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-slice-command-usage) | Automatically slice COCO annotation and image files — see [slicing utilities](docs/slicing.md) | | [coco fiftyone](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-fiftyone-command-usage) | Explore multiple prediction results on your COCO dataset with [fiftyone ui](https://github.com/voxel51/fiftyone) ordered by number of misdetections | | [coco evaluate](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-evaluate-command-usage) | Evaluate classwise COCO AP and AR for given predictions and ground truth — check [COCO utilities](docs/coco.md) | | [coco analyse](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-analyse-command-usage) | Calculate and export many error analysis plots — see the [complete guide](docs/README.md) | | [coco yolo](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-yolo-command-usage) | Automatically convert any COCO dataset to [ultralytics](https://github.com/ultralytics/ultralytics) format | ### Approved by the Community [📜 List of publications that cite SAHI (currently 600+)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0,5&cites=14065474760484865747&scipsc=&q=&scisbd=1) [🏆 List of competition winners that used SAHI](https://github.com/obss/sahi/discussions/688) ### Approved by AI Tools SAHI's documentation is [indexed in Context7 MCP](https://context7.com/obss/sahi), providing AI coding assistants with up-to-date, version-specific code examples and API references. We also provide an [llms.txt](https://context7.com/obss/sahi/llms.txt) file following the emerging standard for AI-readable documentation. To integrate SAHI docs with your AI development workflow, check out the [Context7 MCP installation guide](https://github.com/upstash/context7#%EF%B8%8F-installation). ##
Installation
### Basic Installation ```bash pip install sahi ```
Detailed Installation (Click to open) - Install your desired version of pytorch and torchvision: ```console pip install torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu126 ``` (torch 2.1.2 is required for mmdet support): ```console pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121 ``` - Install your desired detection framework (ultralytics): ```console pip install ultralytics>=8.3.161 ``` - Install your desired detection framework (huggingface): ```console pip install transformers>=4.49.0 timm ``` - Install your desired detection framework (yolov5): ```console pip install yolov5==7.0.14 sahi==0.11.21 ``` - Install your desired detection framework (mmdet): ```console pip install mim mim install mmdet==3.3.0 ``` - Install your desired detection framework (roboflow): ```console pip install inference>=0.51.5 rfdetr>=1.6.2 ```
##
Quick Start
### Learning Resources | Resource | Type | | --------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | | [Introduction to SAHI](https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80) | Blog Post | | [2025 Video Tutorial](https://www.youtube.com/watch?v=ILqMBah5ZvI) ⭐ | Video | | [Official Paper](https://ieeexplore.ieee.org/document/9897990) (ICIP 2022 oral) | Paper | | [Pretrained Weights & ICIP 2022 Paper Files](https://github.com/fcakyon/small-object-detection-benchmark) | Benchmark | | [Visualizing and Evaluating SAHI Predictions with FiftyOne](https://voxel51.com/blog/how-to-detect-small-objects/) | Blog Post | | [Exploring SAHI – learnopencv.com](https://learnopencv.com/slicing-aided-hyper-inference/) | Article | | [Slicing Aided Hyper Inference Explained by Encord](https://encord.com/blog/slicing-aided-hyper-inference-explained/) | Article | | [Video Tutorial: SAHI for Small Object Detection](https://www.youtube.com/watch?v=UuOJKxn-M8&t=270s) | Video | | [Satellite Object Detection](https://blog.ml6.eu/how-to-detect-small-objects-in-very-large-images-70234bab0f98) | Blog Post | | [COCO Dataset Conversion](https://medium.com/codable/convert-any-dataset-to-coco-object-detection-format-with-sahi-95349e1fe2b7) | Blog Post | | [Kaggle Notebook](https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx) | Notebook | | [Error Analysis Plots & Evaluation](https://github.com/obss/sahi/discussions/622) ⭐ | Discussion | | [Interactive Result Visualization and Inspection](https://github.com/obss/sahi/discussions/624) ⭐ | Discussion | | [Video Inference Support](https://github.com/obss/sahi/discussions/626) | Discussion | | [Slicing Operation Notebook](demo/slicing.ipynb) | Notebook | | [Complete Documentation](docs/README.md) | Docs | ### Notebooks & Demos | Framework | Notebook | Demo | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- | | YOLO12 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_ultralytics.ipynb) | — | | YOLO11 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_ultralytics.ipynb) | — | | YOLO11-OBB | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_ultralytics.ipynb) | — | | Roboflow / RF-DETR | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_roboflow.ipynb) | — | | RT-DETR v2 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_huggingface.ipynb) | — | | RT-DETR | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_rtdetr.ipynb) | — | | HuggingFace | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_huggingface.ipynb) | — | | YOLOv5 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb) | — | | MMDetection | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_mmdetection.ipynb) | — | | TorchVision | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_torchvision.ipynb) | — | | YOLOX | — | [![HuggingFace Spaces](https://raw.githubusercontent.com/obss/sahi/main/resources/hf_spaces_badge.svg)](https://huggingface.co/spaces/fcakyon/sahi-yolox) | sahi-yolox ### Framework Agnostic Sliced/Standard Prediction sahi-predict Find detailed info on using `sahi predict` command in the [CLI documentation](docs/cli.md#predict-command-usage) and explore the [prediction API](docs/predict.md) for advanced usage. Find detailed info on video inference at [video inference tutorial](https://github.com/obss/sahi/discussions/626). ### Error Analysis Plots & Evaluation sahi-analyse Find detailed info at [Error Analysis Plots & Evaluation](https://github.com/obss/sahi/discussions/622). ### Interactive Visualization & Inspection sahi-fiftyone Explore [FiftyOne integration](docs/fiftyone.md) for interactive visualization and inspection. ### Other Utilities Check the [comprehensive COCO utilities guide](docs/coco.md) for YOLO conversion, dataset slicing, subsampling, filtering, merging, and splitting operations. Learn more about the [slicing utilities](docs/slicing.md) for detailed control over image and dataset slicing parameters. ##
Citation
If you use this package in your work, please cite as: ```bibtex @article{akyon2022sahi, title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection}, author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin}, journal={2022 IEEE International Conference on Image Processing (ICIP)}, doi={10.1109/ICIP46576.2022.9897990}, pages={966-970}, year={2022} } ``` ```bibtex @software{obss2021sahi, author = {Akyon, Fatih Cagatay and Cengiz, Cemil and Altinuc, Sinan Onur and Cavusoglu, Devrim and Sahin, Kadir and Eryuksel, Ogulcan}, title = {{SAHI: A lightweight vision library for performing large scale object detection and instance segmentation}}, month = nov, year = 2021, publisher = {Zenodo}, doi = {10.5281/zenodo.5718950}, url = {https://doi.org/10.5281/zenodo.5718950} } ``` ##
Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!