YOLO-World Model

Class: YoloWorldModelBlockV1

Source: inference.core.workflows.core_steps.models.foundation.yolo_world.v1.YoloWorldModelBlockV1

Run YOLO-World, a zero-shot object detection model, on an image.

YOLO-World accepts one or more text classes you want to identify in an image. The model returns the location of objects that meet the specified class, if YOLO-World is able to identify objects of that class.

We recommend experimenting with YOLO-World to evaluate the model on your use case before using this block in production. For example on how to effectively prompt YOLO-World, refer to the Roboflow YOLO-World prompting guide.

Type identifier

Use the following identifier in step "type" field: roboflow_core/yolo_world_model@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
class_names	List[str]	One or more classes that you want YOLO-World to detect. The model accepts any string as an input, though does best with short descriptions of common objects..	✅
version	str	Variant of YoloWorld model.	✅
confidence	float	Confidence threshold for detections.	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to YOLO-World Model in version v1.

• inputs: Polygon Zone Visualization, LMM For Classification, VLM as Classifier, Identify Outliers, Dot Visualization, Morphological Transformation, Size Measurement, Blur Visualization, Perspective Correction, Clip Comparison, Corner Visualization, LMM, Florence-2 Model, Grid Visualization, Image Threshold, Florence-2 Model, Halo Visualization, OpenAI, Multi-Label Classification Model, CogVLM, EasyOCR, Line Counter Visualization, Stitch OCR Detections, Stability AI Outpainting, Twilio SMS Notification, Keypoint Detection Model, Google Vision OCR, Identify Changes, Camera Focus, Roboflow Dataset Upload, SIFT, Email Notification, Clip Comparison, Image Slicer, Image Convert Grayscale, Keypoint Visualization, Instance Segmentation Model, OCR Model, Llama 3.2 Vision, Bounding Box Visualization, Reference Path Visualization, Dynamic Crop, Roboflow Dataset Upload, Mask Visualization, Image Preprocessing, Background Color Visualization, Local File Sink, Webhook Sink, Camera Calibration, OpenAI, Depth Estimation, Image Slicer, QR Code Generator, Trace Visualization, Object Detection Model, Contrast Equalization, Buffer, Crop Visualization, Stability AI Image Generation, Dimension Collapse, SIFT Comparison, Roboflow Custom Metadata, Model Comparison Visualization, Pixelate Visualization, Dynamic Zone, Model Monitoring Inference Aggregator, Anthropic Claude, Relative Static Crop, Image Contours, Polygon Visualization, OpenAI, Slack Notification, Triangle Visualization, Classification Label Visualization, Circle Visualization, Image Blur, Label Visualization, Google Gemini, VLM as Detector, Absolute Static Crop, Stability AI Inpainting, Icon Visualization, Ellipse Visualization, Color Visualization, CSV Formatter, Single-Label Classification Model, Detections Consensus, Stitch Images
• outputs: Byte Tracker, Distance Measurement, Time in Zone, Dot Visualization, Detections Stitch, Size Measurement, Time in Zone, Blur Visualization, Velocity, Perspective Correction, Detections Transformation, Corner Visualization, Trace Visualization, Byte Tracker, Overlap Filter, PTZ Tracking (ONVIF).md), Florence-2 Model, Crop Visualization, Florence-2 Model, Roboflow Custom Metadata, Model Comparison Visualization, Detections Combine, Detection Offset, Pixelate Visualization, Model Monitoring Inference Aggregator, Byte Tracker, Line Counter, Time in Zone, Stitch OCR Detections, Path Deviation, Triangle Visualization, Roboflow Dataset Upload, Detections Filter, Detections Classes Replacement, Circle Visualization, Detections Stabilizer, Label Visualization, Bounding Box Visualization, Line Counter, Icon Visualization, Ellipse Visualization, Dynamic Crop, Color Visualization, Roboflow Dataset Upload, Segment Anything 2 Model, Detections Merge, Path Deviation, Detections Consensus, Background Color Visualization

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds YOLO-World Model in version v1 has.

Bindings

• input

  • images (image): The image to infer on..
  • class_names (list_of_values): One or more classes that you want YOLO-World to detect. The model accepts any string as an input, though does best with short descriptions of common objects..
  • version (string): Variant of YoloWorld model.
  • confidence (float_zero_to_one): Confidence threshold for detections.

• output

  • predictions (object_detection_prediction): Prediction with detected bounding boxes in form of sv.Detections(...) object.

Example JSON definition of step YOLO-World Model in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/yolo_world_model@v1",
    "images": "$inputs.image",
    "class_names": [
        "person",
        "car",
        "license plate"
    ],
    "version": "v2-s",
    "confidence": 0.005
}