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    Models/Detection & Recognition/AILab-CVC/YOLO-World-L
    PyTorchObject DetectionGPL-3.0

    YOLO-World-L

    by AILab-CVC

    Real-time open-vocabulary object detection with text prompts

    Identifiers
    Model ID
    AILab-CVC/YOLO-World-L
    Feature URI
    mixpeek://image_extractor@v1/tencent_yoloworld_large_v1

    Overview

    YOLO-World extends the YOLO detector family with open-vocabulary detection via vision-language modeling. Users specify objects to detect with text prompts; the model finds them zero-shot at real-time speeds (52 FPS on V100).

    On Mixpeek, YOLO-World enables detecting arbitrary objects in video and images using natural language, without retraining for each new category.

    Architecture

    YOLO backbone with Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN). Uses region-text contrastive loss and a prompt-then-detect paradigm where vocabulary is embedded as model parameters for fast inference.

    Mixpeek SDK Integration

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

    • Open-vocabulary detection with text prompts
    • 52 FPS on V100 (real-time)
    • 35.4 AP on LVIS zero-shot
    • Supports image-prompted detection
    • ONNX and TFLite INT8 export

    Use Cases on Mixpeek

    Real-time video monitoring for arbitrary object types
    Content moderation with dynamically defined categories
    Retail inventory tracking with custom product lists
    Open-ended visual question answering pipelines

    Benchmarks

    DatasetMetricScoreSource
    LVIS (zero-shot)AP35.4Cheng et al., 2024: Table 1
    COCO val2017AP45.7Cheng et al., 2024: Table 2

    Performance

    Input Size640×640 px
    GPU Latency~8ms / image (A100)
    CPU Latency~95ms / image
    GPU Throughput~125 images/sec (A100)
    GPU Memory~0.9 GB

    Specification

    FrameworkPyTorch
    OrganizationAILab-CVC
    FeatureObject Detection
    Outputbbox + label
    Modalitiesvideo, image
    RetrieverObject Filter
    Parameters~100M
    LicenseGPL-3.0
    Downloads/mo320K

    Research Paper

    YOLO-World: Real-Time Open-Vocabulary Object Detection

    arxiv.org

    Build a pipeline with YOLO-World-L

    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