{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/6"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/14"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"SAHI performs sliced inference on large images to detect and segment small objects, breaking images into overlapping tiles for processing by detection models and merging results.","skillfed_tags":["object-detection","image-processing","computer-vision"],"use_cases":["Detect small objects in aerial or satellite imagery by slicing large images into manageable tiles.","Improve detection accuracy on crowded scenes where objects are densely packed and small relative to image size.","Preprocess datasets by automatically slicing COCO-annotated images and converting between annotation formats.","Evaluate detection model performance on large images using built-in COCO evaluation and error analysis tools.","Prototype detection pipelines with multiple frameworks without rewriting inference code.","Analyze detection failures across a dataset and export error plots for model debugging."],"what_it_does":"SAHI is a lightweight wrapper library that enables sliced inference\u2014a technique for improving object detection on large images containing small objects. Instead of running detection on the full image at once, SAHI divides the image into overlapping tiles, runs inference on each tile independently, and merges the predictions back together. This approach helps detection models see small objects at higher effective resolution, which often improves accuracy on objects that would otherwise be missed or poorly localized.\n\nThe library integrates with popular detection frameworks (ultralytics, mmdet, huggingface, torchvision, roboflow) and provides both programmatic APIs and command-line tools. It includes utilities for dataset slicing, COCO evaluation, error analysis, and visualization. Runtime dependencies are standard data-processing libraries (numpy, opencv-python, pillow, matplotlib, shapely, requests, pyyaml, click, fire, tqdm), making it straightforward to add to existing computer vision pipelines.","worth_installing":"Yes. SAHI is actively maintained, has no security vulnerabilities, and solves a real problem in object detection on large images. Install it if you work with large images containing small objects or need a framework-agnostic wrapper for detection inference. The low install friction and permissive license make it a low-risk addition to a computer vision project."},"id":"sahi","links":{"html":"https://skillfed.io/packages/sahi","md":"https://skillfed.io/packages/sahi.md","pypi":"https://pypi.org/project/sahi/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-03","license_spdx":"MIT","license_treatment":"permissive","name":"sahi","python_support":"supports_current","summary":"A vision library for performing sliced inference on large images/small objects"},"popularity":{"monthly_downloads":281218,"position":8102,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.12.5"}