Сегментация изображений

Choose the segmentation task before collecting annotations. Labeling every road pixel is different from identifying each individual vehicle. Define how to treat uncertain boundaries, transparent objects, overlapping instances, and regions outside the label set. Annotation quality affects the result. Two reviewers may draw different boundaries around hair, shadows, or partially visible objects. Document the labeling convention and measure disagreements rather than assuming there is always one perfectly obvious mask. Use metrics suited to the application. Pixel accuracy can look high when most pixels are background. Overlap metrics such as intersection over union can provide more information, but class balance and boundary quality still matter. A small boundary error may be harmless in one task and consequential in another. Test the full image pipeline. Cropping, resizing, and coordinate conversion can shift an otherwise reasonable mask when it is placed back on the original image. Preserve source dimensions and inspect overlays at the scale where the result will be used.