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