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