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

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
}