| Name | sahi JSON |
| Version |
0.11.34
JSON |
| download |
| home_page | None |
| Summary | A vision library for performing sliced inference on large images/small objects |
| upload_time | 2025-08-31 11:03:15 |
| maintainer | Fatih Cagatay Akyon (@fcakyon) |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="center">
<h1>
SAHI: Slicing Aided Hyper Inference
</h1>
<h4>
A lightweight vision library for performing large scale object detection & instance segmentation
</h4>
<h4>
<img width="700" alt="teaser" src="https://raw.githubusercontent.com/obss/sahi/main/resources/sliced_inference.gif">
</h4>
<div>
<a href="https://pepy.tech/project/sahi"><img src="https://pepy.tech/badge/sahi" alt="downloads"></a>
<a href="https://pepy.tech/project/sahi"><img src="https://pepy.tech/badge/sahi/month" alt="downloads"></a>
<a href="https://github.com/obss/sahi/blob/main/LICENSE.md"><img src="https://img.shields.io/pypi/l/sahi" alt="License"></a>
<a href="https://badge.fury.io/py/sahi"><img src="https://badge.fury.io/py/sahi.svg" alt="pypi version"></a>
<a href="https://anaconda.org/conda-forge/sahi"><img src="https://anaconda.org/conda-forge/sahi/badges/version.svg" alt="conda version"></a>
<a href="https://github.com/obss/sahi/actions/workflows/ci.yml"><img src="https://github.com/obss/sahi/actions/workflows/ci.yml/badge.svg" alt="Continuous Integration"></a>
<br>
<a href="https://context7.com/obss/sahi"><img src="https://img.shields.io/badge/Context7%20MCP-Indexed-blue" alt="Context7 MCP"></a>
<a href="https://context7.com/obss/sahi/llms.txt"><img src="https://img.shields.io/badge/llms.txt-✓-brightgreen" alt="llms.txt"></a>
<a href="https://ieeexplore.ieee.org/document/9897990"><img src="https://img.shields.io/badge/DOI-10.1109%2FICIP46576.2022.9897990-orange.svg" alt="ci"></a>
<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="Open In Colab"></a>
<a href="https://huggingface.co/spaces/fcakyon/sahi-yolox"><img src="https://raw.githubusercontent.com/obss/sahi/main/resources/hf_spaces_badge.svg" alt="HuggingFace Spaces"></a>
<a href="https://deepwiki.com/obss/sahi"><img src="https://img.shields.io/badge/DeepWiki-obss%2Fsahi-blue.svg?logo=data:image/png;base64,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" alt="Sliced/tiled inference DeepWiki"></a>
<a href="https://squidfunk.github.io/mkdocs-material/"><img src="https://img.shields.io/badge/Material_for_MkDocs-526CFE?logo=MaterialForMkDocs&logoColor=white" alt="built-with-material-for-mkdocs"></a>
</div>
</div>
## <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](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 400+)](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).
## <div align="center">Installation</div>
### Basic Installation
```bash
pip install sahi
```
<details closed>
<summary>
<big><b>Detailed Installation (Click to open)</b></big>
</summary>
- 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>=0.50.3 rfdetr>=1.1.0
```
</details>
## <div align="center">Quick Start</div>
### Tutorials
- [Introduction to SAHI](https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80) - explore the [complete documentation](docs/README.md) for advanced usage
- [Official paper](https://ieeexplore.ieee.org/document/9897990) (ICIP 2022 oral)
- [Pretrained weights and ICIP 2022 paper files](https://github.com/fcakyon/small-object-detection-benchmark)
- [2025 Video Tutorial](https://www.youtube.com/watch?v=ILqMBah5ZvI) (RECOMMENDED)
- [Visualizing and Evaluating SAHI predictions with FiftyOne](https://voxel51.com/blog/how-to-detect-small-objects/)
- ['Exploring SAHI' Research Article from 'learnopencv.com'](https://learnopencv.com/slicing-aided-hyper-inference/)
- [Slicing Aided Hyper Inference Explained by Encord](https://encord.com/blog/slicing-aided-hyper-inference-explained/)
- ['VIDEO TUTORIAL: Slicing Aided Hyper Inference for Small Object Detection - SAHI'](https://www.youtube.com/watch?v=UuOJKxn-M8&t=270s)
- [Video inference support is live](https://github.com/obss/sahi/discussions/626)
- [Kaggle notebook](https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx)
- [Satellite object detection](https://blog.ml6.eu/how-to-detect-small-objects-in-very-large-images-70234bab0f98)
- [Error analysis plots & evaluation](https://github.com/obss/sahi/discussions/622) (RECOMMENDED)
- [Interactive result visualization and inspection](https://github.com/obss/sahi/discussions/624) (RECOMMENDED)
- [COCO dataset conversion](https://medium.com/codable/convert-any-dataset-to-coco-object-detection-format-with-sahi-95349e1fe2b7)
- [Slicing operation notebook](demo/slicing.ipynb)
- `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>
- `YOLO12` + `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-yolo12"></a>
- `YOLO11-OBB` + `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-yolo11-obb"></a> (NEW)
- `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-yolo11"></a>
- `Roboflow/RF-DETR` + `SAHI` walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_roboflow.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="roboflow"></a> (NEW)
- `RT-DETR v2` + `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-rtdetrv2"></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>
- `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>
- `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>
### 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](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
<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](https://github.com/obss/sahi/discussions/622).
### 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](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.
## <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](CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
<p align="center">
<a href="https://github.com/obss/sahi/graphs/contributors">
<img src="https://contrib.rocks/image?repo=obss/sahi" />
</a>
</p>
Raw data
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"description": "<div align=\"center\">\n<h1>\n SAHI: Slicing Aided Hyper Inference\n</h1>\n\n<h4>\n A lightweight vision library for performing large scale object detection & instance segmentation\n</h4>\n\n<h4>\n <img width=\"700\" alt=\"teaser\" src=\"https://raw.githubusercontent.com/obss/sahi/main/resources/sliced_inference.gif\">\n</h4>\n\n<div>\n <a href=\"https://pepy.tech/project/sahi\"><img src=\"https://pepy.tech/badge/sahi\" alt=\"downloads\"></a>\n <a href=\"https://pepy.tech/project/sahi\"><img src=\"https://pepy.tech/badge/sahi/month\" alt=\"downloads\"></a>\n <a href=\"https://github.com/obss/sahi/blob/main/LICENSE.md\"><img src=\"https://img.shields.io/pypi/l/sahi\" alt=\"License\"></a>\n <a href=\"https://badge.fury.io/py/sahi\"><img src=\"https://badge.fury.io/py/sahi.svg\" alt=\"pypi version\"></a>\n <a href=\"https://anaconda.org/conda-forge/sahi\"><img src=\"https://anaconda.org/conda-forge/sahi/badges/version.svg\" alt=\"conda version\"></a>\n <a href=\"https://github.com/obss/sahi/actions/workflows/ci.yml\"><img src=\"https://github.com/obss/sahi/actions/workflows/ci.yml/badge.svg\" alt=\"Continuous Integration\"></a>\n <br>\n <a href=\"https://context7.com/obss/sahi\"><img src=\"https://img.shields.io/badge/Context7%20MCP-Indexed-blue\" alt=\"Context7 MCP\"></a>\n <a href=\"https://context7.com/obss/sahi/llms.txt\"><img src=\"https://img.shields.io/badge/llms.txt-\u2713-brightgreen\" alt=\"llms.txt\"></a>\n <a href=\"https://ieeexplore.ieee.org/document/9897990\"><img src=\"https://img.shields.io/badge/DOI-10.1109%2FICIP46576.2022.9897990-orange.svg\" alt=\"ci\"></a>\n <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=\"Open In Colab\"></a>\n <a href=\"https://huggingface.co/spaces/fcakyon/sahi-yolox\"><img src=\"https://raw.githubusercontent.com/obss/sahi/main/resources/hf_spaces_badge.svg\" alt=\"HuggingFace Spaces\"></a>\n <a href=\"https://deepwiki.com/obss/sahi\"><img src=\"https://img.shields.io/badge/DeepWiki-obss%2Fsahi-blue.svg?logo=data:image/png;base64,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\" alt=\"Sliced/tiled inference DeepWiki\"></a>\n <a href=\"https://squidfunk.github.io/mkdocs-material/\"><img src=\"https://img.shields.io/badge/Material_for_MkDocs-526CFE?logo=MaterialForMkDocs&logoColor=white\" alt=\"built-with-material-for-mkdocs\"></a>\n\n</div>\n</div>\n\n## <div align=\"center\">Overview</div>\n\nSAHI 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.\n\n| Command | Description |\n|---|---|\n| [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) |\n| [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) |\n| [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) |\n| [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 |\n| [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) |\n| [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) |\n| [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 |\n\n### Approved by the Community\n\n[\ud83d\udcdc List of publications that cite SAHI (currently 400+)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0,5&cites=14065474760484865747&scipsc=&q=&scisbd=1)\n\n[\ud83c\udfc6 List of competition winners that used SAHI](https://github.com/obss/sahi/discussions/688)\n\n### Approved by AI Tools\nSAHI'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).\n\n## <div align=\"center\">Installation</div>\n\n### Basic Installation\n```bash\npip install sahi\n```\n\n<details closed>\n<summary>\n<big><b>Detailed Installation (Click to open)</b></big>\n</summary>\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://download.pytorch.org/whl/cu126\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://download.pytorch.org/whl/cu121\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>=0.50.3 rfdetr>=1.1.0\n```\n\n</details>\n\n## <div align=\"center\">Quick Start</div>\n\n### Tutorials\n\n- [Introduction to SAHI](https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80) - explore the [complete documentation](docs/README.md) for advanced usage\n\n- [Official paper](https://ieeexplore.ieee.org/document/9897990) (ICIP 2022 oral)\n\n- [Pretrained weights and ICIP 2022 paper files](https://github.com/fcakyon/small-object-detection-benchmark)\n\n- [2025 Video Tutorial](https://www.youtube.com/watch?v=ILqMBah5ZvI) (RECOMMENDED)\n\n- [Visualizing and Evaluating SAHI predictions with FiftyOne](https://voxel51.com/blog/how-to-detect-small-objects/)\n\n- ['Exploring SAHI' Research Article from 'learnopencv.com'](https://learnopencv.com/slicing-aided-hyper-inference/)\n\n- [Slicing Aided Hyper Inference Explained by Encord](https://encord.com/blog/slicing-aided-hyper-inference-explained/)\n\n- ['VIDEO TUTORIAL: Slicing Aided Hyper Inference for Small Object Detection - SAHI'](https://www.youtube.com/watch?v=UuOJKxn-M8&t=270s)\n\n- [Video inference support is live](https://github.com/obss/sahi/discussions/626)\n\n- [Kaggle notebook](https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx)\n\n- [Satellite object detection](https://blog.ml6.eu/how-to-detect-small-objects-in-very-large-images-70234bab0f98)\n\n- [Error analysis plots & evaluation](https://github.com/obss/sahi/discussions/622) (RECOMMENDED)\n\n- [Interactive result visualization and inspection](https://github.com/obss/sahi/discussions/624) (RECOMMENDED)\n\n- [COCO dataset conversion](https://medium.com/codable/convert-any-dataset-to-coco-object-detection-format-with-sahi-95349e1fe2b7)\n\n- [Slicing operation notebook](demo/slicing.ipynb)\n\n- `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>\n\n- `YOLO12` + `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-yolo12\"></a>\n\n- `YOLO11-OBB` + `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-yolo11-obb\"></a> (NEW)\n\n- `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-yolo11\"></a>\n\n- `Roboflow/RF-DETR` + `SAHI` walkthrough: <a href=\"https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_roboflow.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"roboflow\"></a> (NEW)\n\n- `RT-DETR v2` + `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-rtdetrv2\"></a> (NEW)\n\n- `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>\n\n- `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>\n\n- `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>\n\n- `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>\n\n- `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>\n\n<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>\n\n### Framework Agnostic Sliced/Standard Prediction\n\n<img width=\"700\" alt=\"sahi-predict\" src=\"https://user-images.githubusercontent.com/34196005/149310540-e32f504c-6c9e-4691-8afd-59f3a1a457f0.gif\">\n\nFind 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.\n\nFind detailed info on video inference at [video inference tutorial](https://github.com/obss/sahi/discussions/626).\n\n### Error Analysis Plots & Evaluation\n\n<img width=\"700\" alt=\"sahi-analyse\" src=\"https://user-images.githubusercontent.com/34196005/149537858-22b2e274-04e8-4e10-8139-6bdcea32feab.gif\">\n\nFind detailed info at [Error Analysis Plots & Evaluation](https://github.com/obss/sahi/discussions/622).\n\n### Interactive Visualization & Inspection\n\n<img width=\"700\" alt=\"sahi-fiftyone\" src=\"https://user-images.githubusercontent.com/34196005/149321540-e6dd5f3-36dc-4267-8574-a985dd0c6578.gif\">\n\nExplore [FiftyOne integration](docs/fiftyone.md) for interactive visualization and inspection.\n\n### Other utilities\n\nCheck 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.\n\n## <div align=\"center\">Citation</div>\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/ICIP46576.2022.9897990},\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/zenodo.5718950},\n url = {https://doi.org/10.5281/zenodo.5718950}\n}\n```\n\n## <div align=\"center\">Contributing</div>\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) to get started. Thank you \ud83d\ude4f to all our contributors!\n\n<p align=\"center\">\n <a href=\"https://github.com/obss/sahi/graphs/contributors\">\n <img src=\"https://contrib.rocks/image?repo=obss/sahi\" />\n </a>\n</p>\n",
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