jcjohnson/densecap

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1596 stars · 425 forks observed · 2026-08-28

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

DenseCap is a Torch implementation of a fully convolutional localization network that performs dense captioning: detecting objects in images and generating natural-language descriptions for each region. It includes a pretrained model trained on Visual Genome, inference code for CPU/GPU, webcam demo, evaluation, and training code.

Use cases

• generate captions for regions of an image
• detect and describe objects in photos
• run dense captioning on new images
• train a dense captioning model on Visual Genome
• evaluate dense captioning models
• run a live webcam captioning demo

When to choose

• you need a research-grade dense captioning model with pretrained weights
• you want to reproduce or build on the CVPR 2016 DenseCap paper
• you can work with the legacy Torch (Lua) ecosystem

When to avoid

• you need a maintained tool or modern framework support (Torch is deprecated)
• you want PyTorch or TensorFlow implementations
• you need production deployment with active support

Facets

library · maturity abandoned

machine-learning deep-learning computer-vision image-processing nlp computer-vision deep-learning machine-learning artificial-intelligence python dense-captioning torch object-detection image-captioning research-code cvpr-2016 visual-genome linux macos gpu

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markdown · JSON · MCP: product_card(name="jcjohnson/densecap")