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name: grounding-dino description: "Use GroundingDINO for open-vocabulary object detection, single-image inference, COCO evaluation, pseudo-label dataset creation, and safe web or downstream integrations." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

GroundingDINO Repo Skill

Use this repo skill when a user asks about the groundingdino Python package, GroundingDINO open-set object detection, Grounding DINO config/checkpoint workflows, or the original demo capabilities from the IDEA-Research GroundingDINO project.

Choose A Route

• sub-skills/inference/: single-image CLI/API inference, load_model, load_image, predict, annotate, the Model wrapper, class-list prompts, token spans, thresholds, devices, and output boxes.
• sub-skills/evaluation/: COCO-style zero-shot AP evaluation, category-prompt construction, CocoGroundingEvaluator, pycocotools, benchmark caveats, and low-AP diagnosis.
• sub-skills/dataset-annotation/: pseudo-label an image folder, draw review images, export COCO-format data, and manage optional FiftyOne workflow dependencies.
• sub-skills/integrations/: Gradio apps, Hugging Face checkpoint UX, Grounded-SAM/SAM handoffs, Stable Diffusion or GLIGEN notebook-style integrations, and color/box contracts.

Quick Install Check

GroundingDINO is a PyTorch package named groundingdino. A minimal install typically needs:

pip install -e .

For a reusable project or app, verify the installed package before running model code:

python scripts/check_grounding_dino_install.py

Read references/install-and-models.md before changing dependencies, compiling custom ops, selecting a config/checkpoint pair, or interpreting CPU-only warnings.

Core Facts

• The packaged model configs are GroundingDINO_SwinT_OGC.py and GroundingDINO_SwinB_cfg.py; both use modelname="groundingdino", num_queries=900, max_text_len=256, and bert-base-uncased text encoding.
• Released workflows require a config file and a matching checkpoint file; this skill does not download checkpoints unless an integration helper explicitly receives Hugging Face download arguments.
• The public inference API returns normalized cxcywh boxes, logits/confidence scores, and phrases; annotation helpers convert boxes for visualization.
• Training code is not part of this repo skill because the source README lists training-code release as incomplete.

Shared References

• references/api-overview.md: package modules, public API surfaces, and where detailed signatures live.
• references/install-and-models.md: install/build behavior, dependencies, model variants, checkpoints, and device guidance.
• references/prompt-and-threshold-guide.md: prompt punctuation, class lists, thresholds, token spans, and no-detection diagnosis.
• references/troubleshooting.md: cross-cutting install, import, model, data, device, and workflow failures.
• references/repo-provenance.md: source commit, dirty-state baseline, package version, and evidence paths.
• references/repo-routing-metadata.json: structured DisCo router metadata used during managed import.