ashkamath/mdetr

Health v2 · maintenance only

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1052 stars · 138 forks observed · 2026-08-28

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

MDETR (Modulated Detection) is a PyTorch research codebase for end-to-end multi-modal object detection that grounds free-form text queries to image regions. It includes pre-trained weights and fine-tuning scripts for phrase grounding, referring expression comprehension/segmentation, and visual question answering.

Use cases

• detect objects mentioned in a free-form text query within an image
• phrase grounding on Flickr30k
• referring expression comprehension on RefCOCO datasets
• referring expression segmentation with PhraseCut
• pre-train a vision-language detector on aligned image-text data
• fine-tune a detector for visual question answering tasks

When to choose

• you need text-conditioned object detection or phrase grounding with pre-trained models
• you are reproducing or building on the MDETR paper for multi-modal understanding research
• you want open-vocabulary detection where class labels come from the text query

When to avoid

• you need a production-ready, actively maintained detection library
• you only need standard class-labeled object detection without text queries
• you lack GPU resources, since training requires multi-node distributed setups

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision nlp computer-vision machine-learning artificial-intelligence python multimodal object-detection phrase-grounding referring-expression-comprehension vision-language research-code pytorch natural-language-processing linux gpu

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markdown · JSON · MCP: product_card(name="ashkamath/mdetr")