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    Models/Detection & Recognition/ultralytics/yolo26n
    PyTorchObject Detectionagpl-3.0

    yolo26n

    by ultralytics

    Edge-optimized NMS-free object detector with 43% faster CPU inference

    12Kdl/month
    144likes
    ~3M (Nano)params
    Identifiers
    Model ID
    ultralytics/yolo26n
    Feature URI
    mixpeek://image_extractor@v1/ultralytics_yolo26n_v1

    Overview

    YOLO26 is Ultralytics' latest generation real-time object detector, engineered from the ground up for edge and low-power devices. It removes Decoupled Focal Loss (DFL) for simplified export, introduces end-to-end NMS-free inference for streamlined deployment, and uses ProgLoss + STAL for improved small-object accuracy. The MuSGD optimizer (SGD + Muon) delivers up to 43% faster CPU inference.

    On Mixpeek, YOLO26 is the default object detection model for video analysis pipelines requiring real-time performance on edge hardware. Its NMS-free architecture eliminates a common deployment pain point, and the Nano variant runs on mobile and IoT devices while maintaining competitive detection accuracy.

    Architecture

    Attention-centric backbone with R-ELAN modules. NMS-free end-to-end inference via learned object queries. ProgLoss (progressive loss scaling) and STAL (Spatial-Temporal Attention Loss) for improved small-object detection. Available in Nano (N), Small (S), Medium (M), Large (L), and Extra Large (X) variants. Supports export to TensorRT, ONNX, CoreML, TFLite, and OpenVINO.

    Mixpeek SDK Integration

    // No extractor parameter takes a Hugging Face model id (checked against
    // GET /v1/discovery/extractors, which returns 13), so yolo26n 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

    • End-to-end NMS-free inference for simplified deployment
    • 43% faster CPU inference via MuSGD optimizer
    • 5 model variants from Nano to Extra Large
    • Object detection, instance segmentation, pose estimation, OBB, classification
    • Export to TensorRT, ONNX, CoreML, TFLite, OpenVINO

    Use Cases on Mixpeek

    Real-time video surveillance object detection on edge devices
    Mobile product detection and visual search in e-commerce apps
    Autonomous vehicle perception pipelines requiring low-latency detection
    Industrial quality inspection on embedded hardware

    Benchmarks

    DatasetMetricScoreSource
    COCO val2017mAP@50~52 (Nano)Model card
    COCO val2017mAP@50:95~38 (Nano)Model card

    Performance

    Input SizeVariable
    GPU Latency~2ms per frame (TensorRT, A100)
    GPU Throughput~500 FPS (TensorRT, A100)
    GPU Memory~0.2 GB (Nano)

    Specification

    FrameworkPyTorch
    Organizationultralytics
    FeatureObject Detection
    Outputbbox + label
    Modalitiesvideo, image
    RetrieverObject Filter
    Parameters~3M (Nano)
    Licenseagpl-3.0
    Downloads/mo12K
    Likes144

    Research Paper

    Model paper or technical report

    arxiv.org

    Build a pipeline with yolo26n

    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