VDIGPKU/M2Det

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1438 stars · 313 forks observed · 2026-08-28

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

M2Det is a PyTorch implementation of a single-shot object detector based on a Multi-Level Feature Pyramid Network (MLFPN), published at AAAI 2019. It provides training, evaluation, and demo code for detecting objects while handling both scale and appearance-complexity variation.

Use cases

• train a custom object detection model
• run single-shot object detection on images
• research multi-level feature pyramid networks
• benchmark object detectors on COCO-style datasets
• detect objects of varying appearance complexity
• reproduce AAAI 2019 paper results

When to choose

• you need a research-grade single-shot detector with multi-level feature fusion
• you want to experiment with or extend MLFPN architectures
• you're reproducing or building on the M2Det paper

When to avoid

• you need a maintained production detector with recent updates
• you want the latest state-of-the-art detection accuracy
• you need Windows or non-GPU support out of the box

Facets

library · maturity maintenance

machine-learning computer-vision image-processing computer-vision deep-learning machine-learning python object-detection pytorch single-shot-detector feature-pyramid-network research-code aaai-2019 linux gpu

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markdown · JSON · MCP: product_card(name="VDIGPKU/M2Det")