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    Models/Detection & Recognition/IDEA-Research/grounding-dino-base
    HFObject Detectionapache-2.0

    grounding-dino-base

    by IDEA-Research

    Open-set detection using natural language descriptions

    1.5Mdl/month
    203likes
    233Mparams
    Identifiers
    Model ID
    IDEA-Research/grounding-dino-base
    Feature URI
    mixpeek://image_extractor@v1/idea_grounding_dino_base_v1

    Overview

    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

    Open-vocabulary object detection in video surveillance
    Content tagging with arbitrary category sets
    Visual grounding for question answering
    Automated annotation for training data generation

    Benchmarks

    DatasetMetricScoreSource
    COCO val2017 (zero-shot)AP48.4Liu et al., 2024: Table 1
    RefCOCO (val)Accuracy89.2%Liu et al., 2024: Table 3

    Performance

    Input Size800×1333 px
    GPU Latency~32ms / image (A100)
    CPU Latency~410ms / image
    GPU Throughput~31 images/sec (A100)
    GPU Memory~1.6 GB

    Specification

    FrameworkHF
    OrganizationIDEA-Research
    FeatureObject Detection
    Outputbbox + label
    Modalitiesvideo, image
    RetrieverObject Filter
    Parameters233M
    Licenseapache-2.0
    Downloads/mo1.5M
    Likes203

    Research Paper

    Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

    arxiv.org

    Build 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