Image Segmentation
ultralytics
YOLO26
instance-segmentation
shock-wave
shadowgraph
fluid-dynamics
computer-vision
Instructions to use igor3357/shock_waves_segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use igor3357/shock_waves_segmentation with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("igor3357/shock_waves_segmentation") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
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README.md
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---
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library_name: ultralytics
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pipeline_tag: image-segmentation
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tags:
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- YOLO26
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- ultralytics
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- shadowgraph
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- fluid-dynamics
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- computer-vision
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license: apache-2.0
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---
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# YOLO26 Model for Shock-Wave Segmentation
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The model identifies visible shock-wave structures and returns instance segmentation masks. It is intended for automated processing of high-speed flow-visualization data in gas dynamics and shock-wave experiments.
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## Repository Files
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- `shock_seg_model_YOLO26.pt` — trained YOLO26 segmentation model.
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- **Input type:** Experimental shadowgraph images
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- **Output:** Shock-wave instance masks
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- **Weights:** `shock_seg_model_YOLO26.pt`
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## Limitations
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If you use the exact model weights or the accompanying script, please cite this model repository:
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```bibtex
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@misc{
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author = {Doroshchenko, Igor
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title = {YOLO26 Model for Shock-Wave Segmentation},
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year = {2026},
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publisher = {Hugging Face},
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```
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For work focused specifically on the released YOLO26 weights, cite the model repository. For broader methodological and physical context, cite the most relevant publication above in addition to the repository.
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---
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library_name: ultralytics
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pipeline_tag: image-segmentation
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license: apache-2.0
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tags:
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- YOLO26
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- ultralytics
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- shadowgraph
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- fluid-dynamics
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- computer-vision
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---
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# YOLO26 Model for Shock-Wave Segmentation
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The model identifies visible shock-wave structures and returns instance segmentation masks. It is intended for automated processing of high-speed flow-visualization data in gas dynamics and shock-wave experiments.
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**Model authors:** Igor Doroshchenko, Pavel Popov, Aleksei Moiseevskii, and Tahir Kuli-Zade.
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## Repository Files
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- `shock_seg_model_YOLO26.pt` — trained YOLO26 segmentation model.
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- **Input type:** Experimental shadowgraph images
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- **Output:** Shock-wave instance masks
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- **Weights:** `shock_seg_model_YOLO26.pt`
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- **Authors:** Igor Doroshchenko, Pavel Popov, Aleksei Moiseevskii, and Tahir Kuli-Zade
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## Limitations
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If you use the exact model weights or the accompanying script, please cite this model repository:
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```bibtex
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@misc{doroshchenkoEtAl2026shockwaveyolo26,
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author = {Doroshchenko, Igor and Popov, Pavel and Moiseevskii, Aleksei and Kuli-Zade, Tahir},
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title = {YOLO26 Model for Shock-Wave Segmentation},
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year = {2026},
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publisher = {Hugging Face},
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```
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For work focused specifically on the released YOLO26 weights, cite the model repository. For broader methodological and physical context, cite the most relevant publication above in addition to the repository.
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