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

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
}