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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

    Deploy yolo26n

    Single-tenant

    Mixpeek has no managed extractor for this model. On a single-tenant deployment you upload the weights and a custom plugin serves them next to the rest of your pipeline.

    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.

    How it runs

    Inference INPUT MODEL OUTPUT Image object yolo26n mp.inference Detections vector Vector store MVS yolo26n → embeddings, indexed for search
    yolo26n takes image, and Mixpeek indexes what it emits.

    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