{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/8"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"RF-DETR is a transformer-based object detection, instance segmentation, and keypoint detection library built on a DINOv2 vision transformer backbone, designed for real-time inference.","skillfed_tags":["object-detection","computer-vision","transformer-model"],"use_cases":["Build real-time object detection systems for robotics or autonomous systems using the lightweight N/S/M model variants.","Perform instance segmentation on images or video streams where you need both bounding boxes and pixel-level masks.","Integrate keypoint detection (preview) into pose estimation or human activity recognition pipelines.","Benchmark detection accuracy against other SOTA models using the published COCO and RF100-VL results.","Deploy models with neural architecture search to optimize for your specific dataset and hardware constraints."],"what_it_does":"RF-DETR is a production-ready computer vision library for detecting objects, segmenting instances, and detecting keypoints in images using a transformer architecture. It wraps a DINOv2 vision transformer backbone and provides a unified API across all three tasks. The library is built by Roboflow and includes benchmarked model sizes (N, S, M, L) under Apache 2.0, plus larger variants (XL, 2XL) under PML 1.0.\n\nThe package is designed for developers building real-time vision applications who need strong accuracy-latency tradeoffs. It depends on PyTorch, torchvision, transformers, numpy, and several utility libraries (requests, tqdm, pydantic, supervision, pyDeprecate). The library is actively maintained, recently released, and has no known security vulnerabilities.","worth_installing":"Yes. RF-DETR is actively maintained, recently released, has no security vulnerabilities, and offers strong accuracy-latency tradeoffs for object detection and segmentation. The Apache 2.0 base license is permissive. Install friction is low. The main consideration is Python 3.10+ requirement and PyTorch dependency; if your environment already supports those, this is a solid choice for real-time vision tasks."},"id":"rfdetr","links":{"html":"https://skillfed.io/packages/rfdetr","md":"https://skillfed.io/packages/rfdetr.md","pypi":"https://pypi.org/project/rfdetr/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":null,"license_treatment":"permissive","name":"rfdetr","python_support":"supports_current","summary":"RF-DETR"},"popularity":{"monthly_downloads":525564,"position":6185,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.9.2"}