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@v1
to 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
,Camera Focus
,Polygon Zone Visualization
,Slack Notification
,Local File Sink
,Grid Visualization
,Image Convert Grayscale
,Trace Visualization
,Instance Segmentation Model
,Absolute Static Crop
,Roboflow Custom Metadata
,Perspective Correction
,OpenAI
,Circle Visualization
,Dimension Collapse
,Clip Comparison
,Image Slicer
,OpenAI
,Triangle Visualization
,Halo Visualization
,Size Measurement
,Corner Visualization
,Email Notification
,Object Detection Model
,LMM
,Detections Consensus
,Roboflow Dataset Upload
,Dynamic Crop
,Depth Estimation
,Dynamic Zone
,Model Monitoring Inference Aggregator
,Buffer
,Stitch Images
,Llama 3.2 Vision
,Anthropic Claude
,Model Comparison Visualization
,Crop Visualization
,Blur Visualization
,CogVLM
,Image Threshold
,Stability AI Inpainting
,VLM as Detector
,Relative Static Crop
,Image Preprocessing
,Keypoint Visualization
,Background Color Visualization
,Clip Comparison
,Color Visualization
,Twilio SMS Notification
,OCR Model
,Multi-Label Classification Model
,Classification Label Visualization
,Google Vision OCR
,Camera Calibration
,Pixelate Visualization
,Stitch OCR Detections
,Label Visualization
,Image Slicer
,Reference Path Visualization
,Single-Label Classification Model
,Webhook Sink
,Google Gemini
,Roboflow Dataset Upload
,Line Counter Visualization
,Image Blur
,Florence-2 Model
,SIFT Comparison
,LMM For Classification
,Image Contours
,Polygon Visualization
,CSV Formatter
,SIFT
,Florence-2 Model
,Ellipse Visualization
,Mask Visualization
,Keypoint Detection Model
,Bounding Box Visualization
,Dot Visualization
,Stability AI Image Generation
,Identify Outliers
,VLM as Classifier
- outputs:
Detection Offset
,Blur Visualization
,Line Counter
,Detections Filter
,Time in Zone
,Dot Visualization
,Background Color Visualization
,Path Deviation
,Trace Visualization
,Roboflow Custom Metadata
,Color Visualization
,Perspective Correction
,Distance Measurement
,Circle Visualization
,Pixelate Visualization
,Stitch OCR Detections
,Label Visualization
,Triangle Visualization
,Time in Zone
,Roboflow Dataset Upload
,Byte Tracker
,Line Counter
,Size Measurement
,Detections Transformation
,Corner Visualization
,Byte Tracker
,Florence-2 Model
,Detections Stabilizer
,Detections Classes Replacement
,Detections Consensus
,Roboflow Dataset Upload
,Overlap Filter
,Dynamic Crop
,Florence-2 Model
,Detections Merge
,Ellipse Visualization
,Velocity
,Model Monitoring Inference Aggregator
,Detections Stitch
,Segment Anything 2 Model
,Byte Tracker
,Bounding Box Visualization
,Model Comparison Visualization
,Path Deviation
,Crop 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
}