grounding-dino-base
by IDEA-Research
Open-set detection using natural language descriptions
IDEA-Research/grounding-dino-basemixpeek://image_extractor@v1/idea_grounding_dino_base_v1Overview
Grounding DINO combines a DINO-style detection transformer with grounded language understanding for open-set object detection. It achieves 52.5 AP on COCO with zero training data on COCO, and 56.7 AP when fine-tuned.
On Mixpeek, Grounding DINO enables detecting any object by describing it in text. Combined with segmentation models like SAM, it provides a powerful detect-then-segment pipeline.
Architecture
DINO-style detection transformer with Swin backbone, enhanced with text-grounding modules for open-vocabulary detection. Swin-B variant achieves 56.7 AP on COCO.
Mixpeek SDK Integration
// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so grounding-dino-base runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
"https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
{
method: "POST",
headers: {
Authorization: "Bearer API_KEY",
"Content-Type": "application/json",
},
body: JSON.stringify({
collection_id: "col_your_collection",
documents: [
{
document_id: "asset-00412",
// Boxes, masks, depth maps and anomaly scores are structured
// results, not vectors. They go in payload and are reachable
// through pre_filters on a retriever, not through similarity.
payload: {
detections: modelOutput,
source_key: "archive/2026/asset-00412",
},
},
],
}),
},
);
// No managed alternative for an open label set. Two extractors do emit a
// bbox, for the one thing each detects: document_graph_extractor@v1 per
// layout block, face_identity_extractor@v1 per face. Nothing ships that
// returns masks, depth maps or anomaly scores.Capabilities
- Zero-shot detection: 52.5 AP on COCO without COCO training data
- Natural language object descriptions as prompts
- Fine-tuned detection: 56.7 AP (Swin-B)
- Pairs with SAM for detect-then-segment pipelines
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| COCO val2017 (zero-shot) | AP | 48.4 | Liu et al., 2024: Table 1 |
| RefCOCO (val) | Accuracy | 89.2% | Liu et al., 2024: Table 3 |
Performance
Common Pipeline Companions
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Specification
Research Paper
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
arxiv.orgBuild a pipeline with grounding-dino-base
Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.
Run it on your own data, free