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