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    Models/Detection & Recognition/Roboflow/rf-detr-medium
    HFObject Detectionapache-2.0

    rf-detr-medium

    by Roboflow

    Balanced real-time detection transformer for object metadata pipelines

    Identifiers
    Model ID
    Roboflow/rf-detr-medium
    Feature URI
    mixpeek://image_extractor@v1/roboflow_rf_detr_medium_v1

    Overview

    RF-DETR Medium is the balanced checkpoint in Roboflow's real-time detection transformer family. It keeps the DETR-style end-to-end detection formulation while reducing the footprint compared with the Large model.

    On Mixpeek, RF-DETR Medium is a practical default for high-volume video and image pipelines where agents need object metadata for filtering, counting, and follow-up visual inspection without paying for the largest detector on every frame.

    Architecture

    Detection transformer with a ViT-style backbone, multi-scale projection, deformable cross-attention decoder, and DETR-style object queries. The medium checkpoint targets the throughput-quality middle ground.

    Mixpeek SDK Integration

    // No extractor parameter takes a Hugging Face model id (checked against
    // GET /v1/discovery/extractors, which returns 13), so rf-detr-medium 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

    • COCO-style object detection
    • Transformer-based boxes without anchors
    • Balanced latency and quality for production frame pipelines
    • Apache 2.0 license

    Use Cases on Mixpeek

    High-volume video frame object indexing
    Agent filters such as person, vehicle, package, or equipment present
    Retail shelf and warehouse monitoring
    Detection metadata before clip-level retrieval

    Specification

    FrameworkHF
    OrganizationRoboflow
    FeatureObject Detection
    Outputbbox + label
    Modalitiesvideo, image
    RetrieverObject Filter
    Parameters34M
    Licenseapache-2.0
    Downloads/mo1K
    Likes6

    Research Paper

    RF-DETR Medium

    arxiv.org

    Build a pipeline with rf-detr-medium

    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