obss/sahi

Health v2 · maintenance only

Adoption not part of the score

5473 stars · 780 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

SAHI (Slicing Aided Hyper Inference) is a Python vision library for detecting small objects in large images via sliced/tiled inference, working with any object detection or instance segmentation model without retraining. It includes an interactive UI, error analysis plots, COCO dataset utilities, and CLI commands.

Use cases

• detect small objects in large aerial or satellite images
• improve object detection accuracy on high-resolution imagery
• run sliced inference with YOLOv8 or HuggingFace detection models
• tile large images for object detection without retraining the model
• analyze detection errors on COCO-format datasets
• detect tiny objects in drone or surveillance footage

When to choose

• you need better recall on small objects in large images
• you want a model-agnostic slicing pipeline supporting YOLO, MMDetection, Detectron2, or Transformers
• you work with aerial, satellite, or drone imagery
• you need COCO dataset slicing, splitting, and filtering utilities

When to avoid

• your images are small and objects are large - standard inference suffices
• you need real-time low-latency detection, since tiling adds overhead
• your task is classification or keypoint detection rather than detection/segmentation

Facets

library · maturity active

computer-vision image-processing machine-learning cli data-visualization computer-vision deep-learning machine-learning python cli cross-platform object-detection instance-segmentation sliced-inference small-object-detection tiling coco yolo huggingface satellite-imagery aerial-imagery remote-sensing

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