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    Models/Detection & Recognition/ultralytics/yolov8n
    PyTorchObject DetectionAGPL-3.0

    yolov8n

    by ultralytics

    State-of-the-art real-time object detection, YOLO v8 Nano

    Identifiers
    Model ID
    ultralytics/yolov8n
    Feature URI
    mixpeek://image_extractor@v1/ultralytics_yolov8n_v1

    Overview

    YOLOv8n is the nano variant of Ultralytics' YOLOv8 family, optimized for real-time inference while maintaining strong detection accuracy. It supports detection, segmentation, classification, and pose estimation tasks.

    On Mixpeek, YOLOv8n is the fastest object detection option, ideal for processing large video libraries where throughput matters more than detecting every small object.

    Architecture

    CSPDarknet53 backbone with C2f modules, FPN+PAN neck, and decoupled head. Anchor-free detection with distribution focal loss. Nano variant uses depth=0.33, width=0.25.

    Mixpeek SDK Integration

    // No extractor parameter takes a Hugging Face model id (checked against
    // GET /v1/discovery/extractors, which returns 13), so yolov8n 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

    • Real-time inference at 640px resolution
    • 80 COCO object categories
    • Multi-task: detection, segmentation, classification, pose
    • Optimized for edge and mobile deployment

    Use Cases on Mixpeek

    Live video stream monitoring with sub-10ms latency
    Sports analytics, track players, balls, equipment in real-time
    Manufacturing QA, detect defects on production lines

    Benchmarks

    DatasetMetricScoreSource
    COCO val2017mAP50-9537.3Ultralytics YOLOv8 docs
    COCO val2017mAP5052.6Ultralytics YOLOv8 docs

    Performance

    Input Size640×640 px
    GPU Latency~1.2ms / image (A100)
    CPU Latency~18ms / image
    GPU Throughput~830 images/sec (A100)
    GPU Memory~0.3 GB

    3.2M params: nano variant for real-time detection

    Specification

    FrameworkPyTorch
    Organizationultralytics
    FeatureObject Detection
    Outputbbox + label
    Modalitiesvideo, image
    RetrieverObject Filter
    Parameters3.2M
    LicenseAGPL-3.0
    Downloads/moN/A

    Build a pipeline with yolov8n

    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