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

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
}