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https://github.com/obss/sahi
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
https://github.com/obss/sahi
coco computer-vision deep-learning detectron2 explainable-ai fiftyone huggingface instance-segmentation large-image machine-learning merge mmdetection object-detection python pytorch remote-sensing satellite small-object-detection tiling yolov5
Last synced: 5 days ago
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Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
- Host: GitHub
- URL: https://github.com/obss/sahi
- Owner: obss
- License: mit
- Created: 2021-01-30T12:54:53.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-08-27T11:49:25.000Z (4 months ago)
- Last Synced: 2024-10-29T11:17:32.410Z (about 2 months ago)
- Topics: coco, computer-vision, deep-learning, detectron2, explainable-ai, fiftyone, huggingface, instance-segmentation, large-image, machine-learning, merge, mmdetection, object-detection, python, pytorch, remote-sensing, satellite, small-object-detection, tiling, yolov5
- Language: Python
- Homepage: https://ieeexplore.ieee.org/document/9897990
- Size: 61.5 MB
- Stars: 4,052
- Watchers: 44
- Forks: 587
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-cv - SAHI
- awesome-list - SAHI - Platform agnostic sliced/tiled inference + interactive ui + error analysis plots for object detection and instance segmentation. (Computer Vision / Classification & Detection & Tracking)
- awesome-yolo-object-detection - SAHI - tuning for Small Object Detection". (**[arXiv 2022](https://arxiv.org/abs/2202.06934v2), [Zenodo 2021](https://doi.org/10.5281/zenodo.5718950)**). A lightweight vision library for performing large scale object detection/ instance segmentation. SAHI currently supports [YOLOv5 models](https://github.com/ultralytics/yolov5/releases), [MMDetection models](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md), [Detectron2 models](https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md), [HuggingFace models](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads) and [TorchVision models](https://pytorch.org/docs/stable/torchvision/models.html). (Applications)
- awesome-yolo-object-detection - SAHI - tuning for Small Object Detection". (**[arXiv 2022](https://arxiv.org/abs/2202.06934v2), [Zenodo 2021](https://doi.org/10.5281/zenodo.5718950)**). A lightweight vision library for performing large scale object detection/ instance segmentation. SAHI currently supports [YOLOv5 models](https://github.com/ultralytics/yolov5/releases), [MMDetection models](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md), [Detectron2 models](https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md), [HuggingFace models](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads) and [TorchVision models](https://pytorch.org/docs/stable/torchvision/models.html). (Applications)
README
SAHI: Slicing Aided Hyper Inference
A lightweight vision library for performing large scale object detection & instance segmentation
##
Overview
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.
| 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)/[detectron2](https://github.com/facebookresearch/detectron2)/[huggingface](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads)/[torchvision](https://pytorch.org/vision/stable/models.html#object-detection) model |
| [predict-fiftyone](https://github.com/obss/sahi/blob/main/docs/cli.md#predict-fiftyone-command-usage) | perform sliced/standard prediction using any [ultralytics](https://github.com/ultralytics/ultralytics)/[mmdet](https://github.com/open-mmlab/mmdetection)/[detectron2](https://github.com/facebookresearch/detectron2)/[huggingface](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads)/[torchvision](https://pytorch.org/vision/stable/models.html#object-detection) model and explore results in [fiftyone app](https://github.com/voxel51/fiftyone) |
| [coco slice](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-slice-command-usage) | automatically slice COCO annotation and image files |
| [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 |
| [coco analyse](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-analyse-command-usage) | calculate and export many error analysis plots |
| [coco yolov5](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-yolov5-command-usage) | automatically convert any COCO dataset to [ultralytics](https://github.com/ultralytics/ultralytics) format |
##
Quick Start Examples
[📜 List of publications that cite SAHI (currently 200+)](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)
### Tutorials
- [Introduction to SAHI](https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80)
- [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)
- [Visualizing and Evaluating SAHI predictions with FiftyOne](https://voxel51.com/blog/how-to-detect-small-objects/) (2024) (NEW)
- ['Exploring SAHI' Research Article from 'learnopencv.com'](https://learnopencv.com/slicing-aided-hyper-inference/)
- ['VIDEO TUTORIAL: Slicing Aided Hyper Inference for Small Object Detection - SAHI'](https://www.youtube.com/watch?v=UuOjJKxn-M8&t=270s) (RECOMMENDED)
- [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:
- `YOLO11` + `SAHI` walkthrough: (NEW)
- `RT-DETR` + `SAHI` walkthrough: (NEW)
- `YOLOv8` + `SAHI` walkthrough:
- `DeepSparse` + `SAHI` walkthrough:
- `HuggingFace` + `SAHI` walkthrough:
- `YOLOv5` + `SAHI` walkthrough:
- `MMDetection` + `SAHI` walkthrough:
- `Detectron2` + `SAHI` walkthrough:
- `TorchVision` + `SAHI` walkthrough:
### Installation
Installation details:
- Install `sahi` using pip:
```console
pip install sahi
```
- On Windows, `Shapely` needs to be installed via Conda:
```console
conda install -c conda-forge shapely
```
- Install your desired version of pytorch and torchvision (cuda 11.3 for detectron2, cuda 11.7 for rest):
```console
conda install pytorch=1.10.2 torchvision=0.11.3 cudatoolkit=11.3 -c pytorch
```
```console
conda install pytorch=1.13.1 torchvision=0.14.1 pytorch-cuda=11.7 -c pytorch -c nvidia
```
- Install your desired detection framework (yolov5):
```console
pip install yolov5==7.0.13
```
- Install your desired detection framework (ultralytics):
```console
pip install ultralytics==8.3.50
```
- Install your desired detection framework (mmdet):
```console
pip install mim
mim install mmdet==3.0.0
```
- Install your desired detection framework (detectron2):
```console
pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
```
- Install your desired detection framework (huggingface):
```console
pip install transformers timm
```
- Install your desired detection framework (super-gradients):
```console
pip install super-gradients==3.3.1
```
### Framework Agnostic Sliced/Standard Prediction
Find detailed info on `sahi predict` command at [cli.md](docs/cli.md#predict-command-usage).
Find detailed info on video inference at [video inference tutorial](https://github.com/obss/sahi/discussions/626).
Find detailed info on image/dataset slicing utilities at [slicing.md](docs/slicing.md).
### Error Analysis Plots & Evaluation
Find detailed info at [Error Analysis Plots & Evaluation](https://github.com/obss/sahi/discussions/622).
### Interactive Visualization & Inspection
Find detailed info at [Interactive Result Visualization and Inspection](https://github.com/obss/sahi/discussions/624).
### Other utilities
Find detailed info on COCO utilities (yolov5 conversion, slicing, subsampling, filtering, merging, splitting) at [coco.md](docs/coco.md).
Find detailed info on MOT utilities (ground truth dataset creation, exporting tracker metrics in mot challenge format) at [mot.md](docs/mot.md).
##
Citation
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}
}
```
##
Contributing
`sahi` library currently supports all [Ultralytics (YOLOv8/v10/v11/RTDETR) models](https://github.com/ultralytics/ultralytics), [MMDetection models](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md), [Detectron2 models](https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md), and [HuggingFace object detection models](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads). Moreover, it is easy to add new frameworks.
All you need to do is, create a new .py file under [sahi/models/](https://github.com/obss/sahi/tree/main/sahi/models) folder and create a new class in that .py file that implements [DetectionModel class](https://github.com/obss/sahi/blob/aaeb57c39780a5a32c4de2848e54df9a874df58b/sahi/models/base.py#L12). You can take the [MMDetection wrapper](https://github.com/obss/sahi/blob/aaeb57c39780a5a32c4de2848e54df9a874df58b/sahi/models/mmdet.py#L91) or [YOLOv5 wrapper](https://github.com/obss/sahi/blob/7e48bdb6afda26f977b763abdd7d8c9c170636bd/sahi/models/yolov5.py#L17) as a reference.
Before opening a PR:
- Install required development packages:
```bash
pip install -e ."[dev]"
```
- Reformat with black and isort:
```bash
python -m scripts.run_code_style format
```
##
Contributors