SAHI

Use this repo skill when the task involves SAHI (Slicing Aided Hyper Inference) for object detection or instance segmentation on large images, especially sliced inference for small objects, detector-framework wrappers, NMS/NMM duplicate handling, COCO dataset utilities, prediction exports, or the sahi CLI.

SAHI is a Python package. It does not ship detector weights; real inference requires an installed optional detector framework plus local weights, a framework model name, a preloaded model object, or a service/HuggingFace/Roboflow credential where appropriate.

Minimal setup check

pip install sahi
python - <<'PY'
import sahi
from sahi import AutoDetectionModel, ObjectPrediction
from sahi.predict import get_sliced_prediction
print(sahi.__version__)
print(AutoDetectionModel, ObjectPrediction, get_sliced_prediction)
PY

For deeper diagnostics, run the bundled environment checker:

python scripts/check_sahi_env.py --json

Read installation and optional dependencies before installing detector frameworks, GPU packages, COCO evaluation tools, or FiftyOne.

Route by task

User task	Read
Run or debug get_prediction, get_sliced_prediction, predict, sahi predict, folder/video inference, batching, exports, progress callbacks, or slice-size tuning	sliced-inference
Choose/configure AutoDetectionModel.from_pretrained, model type, optional detector framework, local weights, HuggingFace/Roboflow credentials, category mapping, or device	model-integrations
Create, slice, filter, split, merge, convert, evaluate, analyze, or visualize COCO datasets and results	dataset-tools
Choose NMS/NMM/GreedyNMM, IOS vs IOU, class-aware vs class-agnostic matching, or numpy/numba/torchvision postprocess backend	postprocess-backends
Work with BoundingBox, Mask, ObjectPrediction, PredictionResult, COCO/FiftyOne/imantics conversions, visualization helpers, or image/mask utilities	annotations-and-results
Diagnose install/import, optional dependency, OpenCV, device, CLI, data, or credential failures that span several workflows	troubleshooting

High-value entry points

• from sahi import AutoDetectionModel
• from sahi.predict import get_prediction, get_sliced_prediction, predict, predict_fiftyone
• from sahi.slicing import slice_image, slice_coco, get_slice_bboxes
• from sahi.postprocess.backends import set_postprocess_backend, get_postprocess_backend, resolve_backend
• from sahi.postprocess.combine import nms, batched_nms, greedy_nmm, nmm, NMSPostprocess, GreedyNMMPostprocess
• from sahi.annotation import BoundingBox, Category, Mask, ObjectAnnotation
• from sahi.prediction import ObjectPrediction, PredictionResult
• from sahi.utils.coco import Coco, CocoImage, CocoAnnotation, CocoPrediction, export_coco_as_yolo
• CLI groups: sahi predict, sahi predict-fiftyone, sahi coco slice, sahi coco yolo, sahi coco evaluate, sahi coco analyse, sahi coco fiftyone, sahi env, sahi version

Safe bundled checks

Run these when you need deterministic local validation without model downloads, credentials, training, or GPU requirements:

python scripts/check_sahi_env.py
python sub-skills/sliced-inference/scripts/sliced_prediction_smoke.py --mode sliced
python sub-skills/dataset-tools/scripts/coco_fixture_smoke.py
python sub-skills/postprocess-backends/scripts/postprocess_backend_smoke.py --print-backend
python sub-skills/annotations-and-results/scripts/prediction_objects_smoke.py
python sub-skills/model-integrations/scripts/check_model_dependencies.py

The model dependency checker only probes installed packages. It does not prove that a detector checkpoint exists, can download, has credentials, or can run on the requested device.

Evidence and refresh

Read repo provenance before deciding whether this skill is current for another checkout or release. If the package version, public API, CLI entry points, optional dependency matrix, or source commit changed, refresh the skill before relying on exact signatures or troubleshooting notes.

This run intentionally produced a self-contained candidate skill and did not import it into the live DisCo repo-skill library.