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    HFObject Detectionapache-2.0

    yolos-tiny

    by hustvl

    You Only Look at One Sequence, ViT-based real-time object detection

    158Kdl/month
    282likes
    6Mparams
    Identifiers
    Model ID
    hustvl/yolos-tiny
    Feature URI
    mixpeek://image_extractor@v1/hustvl_yolos_tiny_v1

    Overview

    YOLOS adapts the Vision Transformer (ViT) architecture for object detection by simply appending detection tokens to the input sequence. It demonstrates that a pure transformer can perform object detection without any convolutional components.

    On Mixpeek, YOLOS Tiny provides a lightweight, fast alternative to DETR for object detection tasks where speed is prioritized over maximum accuracy.

    Architecture

    Vision Transformer (ViT-Tiny) with 12 layers. Appends 100 learnable detection tokens to the image patch sequence. Uses bipartite matching loss like DETR.

    Mixpeek SDK Integration

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

    • Lightweight ViT-based object detection
    • Fast inference suitable for real-time processing
    • COCO object categories
    • Pure transformer architecture (no CNN backbone)

    Use Cases on Mixpeek

    Real-time video analysis where low latency is critical
    Edge deployment scenarios with limited compute
    High-throughput batch processing of large video archives

    Benchmarks

    DatasetMetricScoreSource
    COCO val2017AP (box)30.4Fang et al., 2021: Table 1
    COCO val2017AP5048.6Fang et al., 2021: Table 1

    Performance

    Input Size512×864 px
    GPU Latency~6ms / image (A100)
    CPU Latency~55ms / image
    GPU Throughput~165 images/sec (A100)
    GPU Memory~0.4 GB

    6.5M params: optimized for edge and high-throughput scenarios

    Specification

    FrameworkHF
    Organizationhustvl
    FeatureObject Detection
    Outputbbox + label
    Modalitiesvideo, image
    RetrieverObject Filter
    Parameters6M
    Licenseapache-2.0
    Downloads/mo158K
    Likes282

    Research Paper

    You Only Look at One Sequence

    arxiv.org

    Build a pipeline with yolos-tiny

    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