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