lufficc/SSD

Health v2 · maintenance only

Adoption not part of the score

1585 stars · 384 forks observed · 2026-08-28

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

A high-quality, fast, modular reference implementation of the SSD (Single Shot MultiBox Detector) object detection model in PyTorch. It supports multi-GPU training and inference, modular component swapping (backbones, heads), CPU inference, and evaluation with metrics visualization via TensorBoard.

Use cases

• train an SSD object detection model on Pascal VOC or COCO
• run object detection inference on images with a pretrained SSD model
• research new SSD variants by swapping backbones or detection heads
• benchmark object detection training speed in PyTorch
• fine-tune object detection on a custom dataset
• visualize training metrics like mAP and AP per category

When to choose

• you need a clean, modular PyTorch codebase for SSD-based object detection research
• you want multi-GPU training with DistributedDataParallel out of the box
• you want to easily replace components like the backbone (e.g., EfficientNet) via config
• you need CPU inference support and automatic pre-trained weight downloads

When to avoid

• you need state-of-the-art detectors like YOLO, DETR, or Faster R-CNN rather than SSD
• you want a production-ready inference service rather than a research codebase
• you need support for frameworks other than PyTorch
• you require active development of new features, as the project is in maintenance mode

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning image-processing python windows object-detection ssd pytorch single-shot-detector reference-implementation multi-gpu-training pascal-voc coco linux macos gpu

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markdown · JSON · MCP: product_card(name="lufficc/SSD")