{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/7"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/15"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"},{"label":"Education","url":"https://skillfed.io/packages/category/education"},{"label":"Image Recognition","url":"https://skillfed.io/packages/category/scientific-engineering-image-recognition"}],"enrichment":{"capability":"YOLOv5 is a packaged object detection model that runs inference on images and video to identify and localize objects, with integrated training, validation, export, and CLI support.","skillfed_tags":["object-detection","computer-vision","pytorch"],"use_cases":["Run inference on images or video to detect and locate objects in real time.","Fine-tune a pre-trained YOLOv5 model on a custom dataset for domain-specific detection tasks.","Export a trained model to ONNX, TorchScript, or TensorFlow Lite for deployment on edge devices.","Stream detections from a webcam or RTSP/RTMP source and save annotated results.","Integrate object detection into a larger ML pipeline via Python API or CLI commands."],"what_it_does":"YOLOv5 is a packaged wrapper around the Ultralytics YOLOv5 object detector, providing pip-installable access to a production-grade real-time detection model. It detects and localizes objects in images, video streams, and webcam feeds, returning bounding boxes, confidence scores, and class predictions. The package includes training and fine-tuning workflows for custom datasets, model export to multiple formats (ONNX, TorchScript, CoreML, TensorFlow Lite), and a CLI for common tasks.\n\nThe package depends on PyTorch, OpenCV, NumPy, and a suite of data-handling and visualization libraries. It integrates with Hugging Face Hub for model sharing, Roboflow for dataset management, and AWS S3 for artifact storage. Training supports COCO-format datasets and Neptune.AI logging. The primary use case is building and deploying object detection pipelines without managing the underlying YOLOv5 repository directly.","worth_installing":"Yes, with conditions. YOLOv5 is actively maintained, production-stable, and has no known vulnerabilities. Install friction is low, but the 23 runtime dependencies\u2014especially torch and torchvision\u2014are heavy; ensure your environment can accommodate them and that GPU support is available if you need inference speed. GPL copyleft licensing requires compliance in derivative works. Suitable for research, prototyping, and applications where GPL terms are acceptable."},"id":"yolov5","links":{"html":"https://skillfed.io/packages/yolov5","md":"https://skillfed.io/packages/yolov5.md","pypi":"https://pypi.org/project/yolov5/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2024-11-11","license_spdx":null,"license_treatment":"copyleft","name":"yolov5","python_support":"supports_current","summary":"Packaged version of the Yolov5 object detector"},"popularity":{"monthly_downloads":241766,"position":8870,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"7.0.14"}