sahi 0.11.36

A vision library for performing sliced inference on large images/small objects

SAHI: Slicing Aided Hyper Inference

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

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.

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 400+)

🏆 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.

Installation

Basic Installation

pip install sahi

Detailed Installation (Click to open)

• Install your desired version of pytorch and torchvision:

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):

pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121

• Install your desired detection framework (ultralytics):

pip install ultralytics>=8.3.161

• Install your desired detection framework (huggingface):

pip install transformers>=4.49.0 timm

• Install your desired detection framework (yolov5):

pip install yolov5==7.0.14 sahi==0.11.21

• Install your desired detection framework (mmdet):

pip install mim
mim install mmdet==3.3.0

• Install your desired detection framework (roboflow):

pip install inference>=0.50.3 rfdetr>=1.1.0

Quick Start

Tutorials

• Introduction to SAHI - explore the complete documentation for advanced usage

• Official paper (ICIP 2022 oral)

• Pretrained weights and ICIP 2022 paper files

• 2025 Video Tutorial (RECOMMENDED)

• Visualizing and Evaluating SAHI predictions with FiftyOne

• 'Exploring SAHI' Research Article from 'learnopencv.com'

• Slicing Aided Hyper Inference Explained by Encord

• 'VIDEO TUTORIAL: Slicing Aided Hyper Inference for Small Object Detection - SAHI'

• Video inference support is live

• Kaggle notebook

• Satellite object detection

• Error analysis plots & evaluation (RECOMMENDED)

• Interactive result visualization and inspection (RECOMMENDED)

• COCO dataset conversion

• Slicing operation notebook

• YOLOX + SAHI demo:

• YOLO12 + SAHI walkthrough:

• YOLO11-OBB + SAHI walkthrough: (NEW)

• YOLO11 + SAHI walkthrough:

• Roboflow/RF-DETR + SAHI walkthrough: (NEW)

• RT-DETR v2 + SAHI walkthrough: (NEW)

• RT-DETR + SAHI walkthrough:

• HuggingFace + SAHI walkthrough:

• YOLOv5 + SAHI walkthrough:

• MMDetection + SAHI walkthrough:

• TorchVision + SAHI walkthrough:

Framework Agnostic Sliced/Standard Prediction

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

Find detailed info at Error Analysis Plots & Evaluation.

Interactive Visualization & Inspection

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.

Citation

If you use this package in your work, please cite as:

@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}
}

@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 to get started. Thank you 🙏 to all our contributors!