Sahi
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
A lightweight vision library for performing large scale object detection & instance segmentation The project is written primarily in Python, distributed under the MIT License license, first published in 2021. It has gained significant community traction with 5,358 stars and 752 forks on GitHub. Key topics include: coco, computer-vision, deep-learning, explainable-ai, fiftyone.
<div align="center">Overview</div>
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.
| Command | Description |
|---|---|
| predict | Perform sliced/standard video/image prediction using any ultralytics / mmdet / huggingface / torchvision model — see CLI guide |
| predict-fiftyone | Perform sliced/standard prediction using any supported model and explore results in fiftyone app — learn more |
| coco slice | Automatically slice COCO annotation and image files — see slicing utilities |
| coco fiftyone | Explore multiple prediction results on your COCO dataset with fiftyone ui ordered by number of misdetections |
| coco evaluate | Evaluate classwise COCO AP and AR for given predictions and ground truth — check COCO utilities |
| coco analyse | Calculate and export many error analysis plots — see the complete guide |
| coco yolo | Automatically convert any COCO dataset to ultralytics format |
Approved by the Community
📜 List of publications that cite SAHI (currently 600+)
🏆 List of competition winners that used SAHI
Approved by AI Tools
SAHI's documentation is
indexed in Context7 MCP, providing AI coding
assistants with up-to-date, version-specific code examples and API references.
We also provide an 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.
<div align="center">Installation</div>
Basic Installation
<details closed> <summary> <big><b>Detailed Installation (Click to open)</b></big> </summary>bashpip install sahi
- Install your desired version of pytorch and torchvision:
consolepip 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):
consolepip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121
- Install your desired detection framework (ultralytics):
consolepip install ultralytics>=8.3.161
- Install your desired detection framework (huggingface):
consolepip install transformers>=4.49.0 timm
- Install your desired detection framework (yolov5):
consolepip install yolov5==7.0.14 sahi==0.12.1
- Install your desired detection framework (mmdet):
consolepip install mim mim install mmdet==3.3.0
- Install your desired detection framework (roboflow):
</details>consolepip install inference>=0.51.5 rfdetr>=1.6.2
<div align="center">Quick Start</div>
Learning Resources
| Resource | Type |
|---|---|
| Introduction to SAHI | Blog Post |
| 2025 Video Tutorial ⭐ | Video |
| Official Paper (ICIP 2022 oral) | Paper |
| Pretrained Weights & ICIP 2022 Paper Files | Benchmark |
| Visualizing and Evaluating SAHI Predictions with FiftyOne | Blog Post |
| Exploring SAHI – learnopencv.com | Article |
| Slicing Aided Hyper Inference Explained by Encord | Article |
| Video Tutorial: SAHI for Small Object Detection | Video |
| Satellite Object Detection | Blog Post |
| COCO Dataset Conversion | Blog Post |
| Kaggle Notebook | Notebook |
| Error Analysis Plots & Evaluation ⭐ | Discussion |
| Interactive Result Visualization and Inspection ⭐ | Discussion |
| Video Inference Support | Discussion |
| Slicing Operation Notebook | Notebook |
| Complete Documentation | Docs |
Notebooks & Demos
| Framework | Notebook | Demo |
|---|---|---|
| YOLO12 |  | — |
| YOLO11 | | — |
| YOLO11-OBB | | — |
| Roboflow / RF-DETR | | — |
| RT-DETR v2 | | — |
| RT-DETR | | — |
| HuggingFace | | — |
| GroundingDINO | | — |
| YOLOv5 | | — |
| MMDetection | | — |
| TorchVision | | — |
| YOLOX | — |  |
<a href="https://huggingface.co/spaces/fcakyon/sahi-yolox"><img width="600" src="https://user-images.githubusercontent.com/34196005/144092739-c1d9bade-a128-4346-947f-424ce00e5c4f.gif" alt="sahi-yolox"></a>
Framework Agnostic Sliced/Standard Prediction
<img width="700" alt="sahi-predict" src="https://user-images.githubusercontent.com/34196005/149310540-e32f504c-6c9e-4691-8afd-59f3a1a457f0.gif">Find detailed info on using sahi predict command in the
CLI documentation and explore the
prediction API for advanced usage.
Find detailed info on video inference at
video inference tutorial.
Error Analysis Plots & Evaluation
<img width="700" alt="sahi-analyse" src="https://user-images.githubusercontent.com/34196005/149537858-22b2e274-04e8-4e10-8139-6bdcea32feab.gif">Find detailed info at
Error Analysis Plots & Evaluation.
Interactive Visualization & Inspection
<img width="700" alt="sahi-fiftyone" src="https://user-images.githubusercontent.com/34196005/149321540-e6dd5f3-36dc-4267-8574-a985dd0c6578.gif">Explore FiftyOne integration for interactive visualization
and inspection.
Other Utilities
Check the comprehensive COCO utilities guide for YOLO
conversion, dataset slicing, subsampling, filtering, merging, and splitting
operations. Learn more about the slicing utilities for
detailed control over image and dataset slicing parameters.
<div align="center">Citation</div>
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} }
<div align="center">Contributing</div>
We welcome contributions! Please see our Contributing Guide
to get started. Thank you 🙏 to all our contributors!
Contributors
Showing top 12 contributors by commit count.
![dependabot[bot]](https://avatars.githubusercontent.com/in/29110?v=4)
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