YOLO-World-L
by AILab-CVC
Real-time open-vocabulary object detection with text prompts
AILab-CVC/YOLO-World-Lmixpeek://image_extractor@v1/tencent_yoloworld_large_v1Overview
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
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| LVIS (zero-shot) | AP | 35.4 | Cheng et al., 2024: Table 1 |
| COCO val2017 | AP | 45.7 | Cheng et al., 2024: Table 2 |
Performance
Common Pipeline Companions
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Specification
Research Paper
YOLO-World: Real-Time Open-Vocabulary Object Detection
arxiv.orgBuild 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