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+Shock Wave Segmentation Dataset
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+This dataset is designed for the segmentation of shock waves in experimental shadowgraph images. It contains images of unsteady gas-dynamic flows with polygon annotations marking visible shock-wave fronts. The annotations are provided in the Ultralytics YOLO segmentation format and are compatible with YOLO26.
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+The dataset can be used to train, validate, and evaluate models for automatic shock-wave detection and segmentation. Potential applications include extracting shock-front geometry, tracking shock-wave motion, measuring curvature and stand-off distance, and automating the processing of large datasets obtained using high-speed flow visualization.
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+Task: Instance segmentation
+Image type: Experimental shadowgraph images
+Class: Shock wave
+Annotation format: YOLO polygon segmentation
+Recommended model: Ultralytics YOLO26
+
+Authors:
+- Igor Doroshchenko
+- Popov Pavel
+- Moiseevskii Aleksei
+
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 license: apache-2.0
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