WongKinYiu/yolor

Health v2 · maintenance only

Adoption not part of the score

2003 stars · 507 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PyTorch implementation of the YOLOR paper 'You Only Learn One Representation: Unified Network for Multiple Tasks', a real-time object detection architecture that unifies implicit and explicit knowledge in a single network. It includes training and inference code, model configs, and pretrained weights with state-of-the-art COCO results.

Use cases

• train a YOLO-family object detection model in PyTorch
• run real-time object detection on images with pretrained weights
• reproduce YOLOR paper results on the COCO benchmark
• fine-tune an object detection model on a custom dataset
• compare YOLOR against YOLOv4 and Scaled-YOLOv4 performance
• study unified implicit and explicit representation learning for detection

When to choose

• You specifically need the YOLOR architecture, its configs, or its pretrained weights for research or reproduction
• You want a high-accuracy real-time detector benchmarked on COCO with published AP/throughput numbers
• You are exploring unified representation learning combining implicit and explicit knowledge in vision models

When to avoid

• You want the newest YOLO models with active development and rich tooling (e.g., YOLOv7, YOLOv8/Ultralytics)
• You need production-grade deployment support such as ONNX/TensorRT export pipelines or extensive documentation
• You need a general-purpose detection framework rather than a specific paper implementation

Facets

library · maturity maintenance

deep-learning machine-learning computer-vision image-processing computer-vision deep-learning machine-learning artificial-intelligence image-processing python cross-platform object-detection yolo pytorch paper-implementation coco darknet real-time-detection unified-network implicit-knowledge pretrained-weights gpu linux

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