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

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
}