Tencent/ObjectDetection-OneStageDet

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1980 stars · 498 forks observed · 2026-08-28

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

OneStageDet (OSD) is a PyTorch-based single-stage object detection framework from Tencent implementing YOLOv2 and YOLOv3 with plans to unify YOLO and SSD in one framework. It includes many efficient backbones (tiny YOLO, MobileNet, ShuffleNet, SqueezeNext, Xception) and benchmarking tools for detection speed.

Use cases

• train a custom YOLOv3 object detector in PyTorch
• benchmark inference speed of detection networks
• experiment with lightweight backbones like MobileNet or ShuffleNet for detection
• reproduce YOLOv2/YOLOv3 results on Pascal VOC
• train tiny YOLO models from scratch for edge deployment

When to choose

• you want a readable PyTorch implementation of YOLOv2/YOLOv3 to study or modify
• you need to compare many efficient backbones within one detection framework
• you want training speed comparable to darknet but in Python

When to avoid

• you need a production-grade, actively maintained detector with modern architectures (e.g., YOLOv8, DETR)
• you require multi-stage detectors or the latest SOTA accuracy
• you need Windows support or recent PyTorch compatibility out of the box

Facets

library · maturity maintenance

machine-learning computer-vision image-processing benchmarking computer-vision deep-learning machine-learning image-processing python object-detection yolo pytorch single-stage-detector yolov3 yolov2 ssd deep-learning-framework linux gpu

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markdown · JSON · MCP: product_card(name="Tencent/ObjectDetection-OneStageDet")