Awesome

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<div align="center">Overview</div>

Object detection and instance segmentation are by far the most important applications in Computer Vision. However, the detection of small objects and inference on large images still need to be improved in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities.

<div align="center">Quick Start Examples</div>

📜 List of publications that cite SAHI (currently 200+)

🏆 List of competition winners that used SAHI

Tutorials

• Introduction to SAHI

• Official paper (ICIP 2022 oral)

• Pretrained weights and ICIP 2022 paper files

• Visualizing and Evaluating SAHI predictions with FiftyOne (2024) (NEW)

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

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

• 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: <a href="https://huggingface.co/spaces/fcakyon/sahi-yolox"><img src="https://raw.githubusercontent.com/obss/sahi/main/resources/hf_spaces_badge.svg" alt="sahi-yolox"></a>

• YOLO11 + SAHI walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_ultralytics.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-yolov8"></a> (NEW)

• RT-DETR + SAHI walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_rtdetr.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-rtdetr"></a> (NEW)

• YOLOv8 + SAHI walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_ultralytics.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-yolov8"></a>

• DeepSparse + SAHI walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_sparse_yolov5.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-deepsparse"></a>

• HuggingFace + SAHI walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_huggingface.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-huggingface"></a>

• YOLOv5 + SAHI walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-yolov5"></a>

• MMDetection + SAHI walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_mmdetection.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-mmdetection"></a>

• Detectron2 + SAHI walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_detectron2.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-detectron2"></a>

• TorchVision + SAHI walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_torchvision.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-torchvision"></a>

<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>

Installation

• Install sahi using pip:

pip install sahi

• On Windows, Shapely needs to be installed via Conda:

conda install -c conda-forge shapely

• Install your desired version of pytorch and torchvision (cuda 11.3 for detectron2, cuda 11.7 for rest):

conda install pytorch=1.10.2 torchvision=0.11.3 cudatoolkit=11.3 -c pytorch

conda install pytorch=1.13.1 torchvision=0.14.1 pytorch-cuda=11.7 -c pytorch -c nvidia

• Install your desired detection framework (yolov5):

pip install yolov5==7.0.13

• Install your desired detection framework (ultralytics):

pip install ultralytics==8.3.50

• Install your desired detection framework (mmdet):

pip install mim
mim install mmdet==3.0.0

• Install your desired detection framework (detectron2):

pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html

• Install your desired detection framework (huggingface):

pip install transformers timm

• Install your desired detection framework (super-gradients):

pip install super-gradients==3.3.1

Framework Agnostic Sliced/Standard Prediction

Find detailed info on sahi predict command at cli.md.

Find detailed info on video inference at video inference tutorial.

Find detailed info on image/dataset slicing utilities at slicing.md.

Error Analysis Plots & Evaluation

Find detailed info at Error Analysis Plots & Evaluation.

Interactive Visualization & Inspection

Find detailed info at Interactive Result Visualization and Inspection.

Other utilities

Find detailed info on COCO utilities (yolov5 conversion, slicing, subsampling, filtering, merging, splitting) at coco.md.

Find detailed info on MOT utilities (ground truth dataset creation, exporting tracker metrics in mot challenge format) at mot.md.

<div align="center">Citation</div>

If you use this package in your work, please cite it 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}
}

<div align="center">Contributing</div>

sahi library currently supports all Ultralytics (YOLOv8/v10/v11/RTDETR) models, MMDetection models, Detectron2 models, and HuggingFace object detection models. Moreover, it is easy to add new frameworks.

All you need to do is, create a new .py file under sahi/models/ folder and create a new class in that .py file that implements DetectionModel class. You can take the MMDetection wrapper or YOLOv5 wrapper as a reference.

Before opening a PR:

• Install required development packages:

pip install -e ."[dev]"

• Reformat with black and isort:

python -m scripts.run_code_style format

<div align="center">Contributors</div>

<a align="left" href="https://github.com/fcakyon" target="_blank">Fatih Cagatay Akyon</a>

<a align="left" href="https://github.com/sinanonur" target="_blank">Sinan Onur Altinuc</a>

<a align="left" href="https://github.com/devrimcavusoglu" target="_blank">Devrim Cavusoglu</a>

<a align="left" href="https://github.com/cemilcengiz" target="_blank">Cemil Cengiz</a>

<a align="left" href="https://github.com/oulcan" target="_blank">Ogulcan Eryuksel</a>

<a align="left" href="https://github.com/kadirnar" target="_blank">Kadir Nar</a>

<a align="left" href="https://github.com/madenburak" target="_blank">Burak Maden</a>

<a align="left" href="https://github.com/PushpakBhoge" target="_blank">Pushpak Bhoge</a>

<a align="left" href="https://github.com/mcvarer" target="_blank">M. Can V.</a>

<a align="left" href="https://github.com/ChristofferEdlund" target="_blank">Christoffer Edlund</a>

<a align="left" href="https://github.com/ishworii" target="_blank">Ishwor</a>

<a align="left" href="https://github.com/mecevit" target="_blank">Mehmet Ecevit</a>

<a align="left" href="https://github.com/ssahinnkadir" target="_blank">Kadir Sahin</a>

<a align="left" href="https://github.com/weypro" target="_blank">Wey</a>

<a align="left" href="https://github.com/youngjae-avikus" target="_blank">Youngjae</a>

<a align="left" href="https://github.com/tureckova" target="_blank">Alzbeta Tureckova</a>

<a align="left" href="https://github.com/s-aiueo32" target="_blank">So Uchida</a>

<a align="left" href="https://github.com/developer0hye" target="_blank">Yonghye Kwon</a>

<a align="left" href="https://github.com/aphilas" target="_blank">Neville</a>

<a align="left" href="https://github.com/mayrajeo" target="_blank">Janne Mäyrä</a>

<a align="left" href="https://github.com/christofferedlund" target="_blank">Christoffer Edlund</a>

<a align="left" href="https://github.com/ilkermanap" target="_blank">Ilker Manap</a>

<a align="left" href="https://github.com/nguyenthean" target="_blank">Nguyễn Thế An</a>

<a align="left" href="https://github.com/weiji14" target="_blank">Wei Ji</a>

<a align="left" href="https://github.com/aynursusuz" target="_blank">Aynur Susuz</a>

<a align="left" href="https://github.com/pranavdurai10" target="_blank">Pranav Durai</a>

<a align="left" href="https://github.com/lakshaymehra" target="_blank">Lakshay Mehra</a>

<a align="left" href="https://github.com/karl-joan" target="_blank">Karl-Joan Alesma</a>

<a align="left" href="https://github.com/jacobmarks" target="_blank">Jacob Marks</a>

<a align="left" href="https://github.com/williamlung" target="_blank">William Lung</a>

<a align="left" href="https://github.com/amoghdhaliwal" target="_blank">Amogh Dhaliwal</a>