"Use GroundingDINO for single-image open-vocabulary detection,
Scanned 9/8/2026
Install to Claude Code
npx -y skills add VectorSpaceLab/AREX-Skill --skill inference --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Inference?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/vectorspacelab-inference-04e63a44)More formats (shields.io, HTML) on the badges page.
---
name: inference
description: "Use GroundingDINO for single-image open-vocabulary detection,
Python inference APIs, token spans, annotation, device selection, and output
interpretation."
disable-model-invocation: true
metadata:
disco-role: operating
license: Apache 2.0
---
# GroundingDINO Inference
Use this sub-skill when a user needs single-image GroundingDINO inference with text prompts, class lists, token spans, annotated output images, or Python API integration.
## Use This For
- Loading a GroundingDINO config/checkpoint and one image for open-vocabulary object detection.
- Choosing between `groundingdino.util.inference` functions and the `Model` wrapper API.
- Building prompts from free-form captions or class lists such as `cat . dog . person .`.
- Using token spans to target exact phrases inside a sentence prompt.
- Interpreting normalized `cxcywh` boxes, pixel `xyxy` annotations, logits/confidence, and phrases.
- Running the bundled CLI helper in `scripts/grounding_dino_infer.py` with explicit input validation.
## Route Elsewhere
- COCO AP and benchmark evaluation: use `../evaluation/`.
- Folder pseudo-label export or COCO-format dataset creation: use `../dataset-annotation/`.
- Gradio, Hugging Face demo, Grounded-SAM, GLIGEN, or image-editing pipelines: use `../integrations/`.
- Training or fine-tuning: treat as an unsupported gap because GroundingDINO training code is not released in this repo skill.
## Quick Start
Prefer the bundled helper for one-off runs because it validates config, checkpoint, image, token spans, thresholds, and device before loading the model:
```bash
python sub-skills/inference/scripts/grounding_dino_infer.py \
--config-file /path/to/GroundingDINO_SwinT_OGC.py \
--checkpoint-path /path/to/groundingdino_swint_ogc.pth \
--image-path /path/to/image.jpg \
--text-prompt "chair . person . dog ." \
--output-dir outputs/grounding-dino \
--box-threshold 0.35 \
--text-threshold 0.25 \
--device cuda \
--json-output
```
For CPU-only checks, add `--cpu-only`. For class-list prompts, use `--classes cat dog "traffic light"` instead of `--text-prompt`. For span-targeted phrases, use `--text-prompt` plus `--token-spans`; see `references/token-spans.md`.
## References
- `references/api-reference.md`: function signatures, wrapper API, inputs, outputs, and box formats.
- `references/workflows.md`: CLI helper recipes, Python API recipes, validation commands, and output interpretation.
- `references/token-spans.md`: token span schema, offset validation, examples, and failure signals.
- `references/troubleshooting.md`: missing files, CPU/CUDA, `_C` custom op errors, no detections, and malformed prompts/spans.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!