{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/2"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence"},{"label":"Image Recognition","url":"https://skillfed.io/packages/category/scientific-engineering-image-recognition"}],"enrichment":{"capability":"Ultralytics YOLO provides a unified framework for training and deploying computer vision models for object detection, instance segmentation, pose estimation, image classification, and semantic segmentation tasks.","skillfed_tags":["computer-vision","object-detection","deep-learning"],"use_cases":["Train a custom object detector on your own dataset and export it to ONNX for deployment in a production service.","Use a pre-trained YOLO model to detect and track objects in video streams or live camera feeds.","Perform pose estimation on images to extract human keypoints for fitness or motion-capture applications.","Build an image classification pipeline that classifies thousands of images using a pre-trained model.","Segment instances in medical or satellite imagery and export results for downstream analysis.","Benchmark model performance across different YOLO variants to choose the best speed-accuracy tradeoff for your hardware."],"what_it_does":"Ultralytics YOLO is a production-grade computer vision framework built around the YOLO family of models (YOLOv3 through YOLO26). It provides a unified Python API and CLI for training, validating, and deploying models across multiple vision tasks: object detection, instance and semantic segmentation, pose estimation, image classification, and oriented object detection. The package wraps PyTorch models and includes utilities for data loading, metric computation, and model export to formats like ONNX.\n\nThe framework is designed for both research and production use, with pre-trained weights available for immediate inference and a training pipeline that accepts custom datasets via YAML configuration. It depends on torch, torchvision, opencv-python, numpy, matplotlib, and several utility libraries for GPU monitoring and platform integration. The AGPL-3.0 license means derivative works must be open-sourced; commercial deployment requires a separate license.","worth_installing":"Yes, with conditions. Ultralytics YOLO is a mature, actively maintained framework ideal for computer vision projects that need object detection, segmentation, or pose estimation. Install it if you need a production-ready YOLO implementation and can accept the AGPL-3.0 license (or obtain a commercial license). The 14 runtime dependencies are substantial\u2014particularly torch and torchvision\u2014so ensure your environment can accommodate them. No known security vulnerabilities as of the latest release."},"id":"ultralytics","links":{"html":"https://skillfed.io/packages/ultralytics","md":"https://skillfed.io/packages/ultralytics.md","pypi":"https://pypi.org/project/ultralytics/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":null,"license_treatment":"agpl","name":"ultralytics","python_support":"supports_current","summary":"Ultralytics YOLO \ud83d\ude80 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation, classification, and oriented object detection."},"popularity":{"monthly_downloads":8965875,"position":1571,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"8.4.120"}