Grid Visualization

Class: GridVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.grid.v1.GridVisualizationBlockV1

The GridVisualization block displays an array of images in a grid. It will automatically resize the images to in the specified width and height. The first image will be in the top left corner, and the rest will be added to the right of the previous image until the row is full.

Type identifier

Use the following identifier in step "type" field: roboflow_core/grid_visualization@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
width	int	Width of the output image..	✅
height	int	Height of the output image..	✅

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 Grid Visualization in version v1.

• inputs: Line Counter, SIFT Comparison, Size Measurement, Line Counter, Florence-2 Model, SIFT Comparison, Template Matching, Pixel Color Count, Image Contours, Clip Comparison, Florence-2 Model, Distance Measurement, Dynamic Zone, Dimension Collapse, Clip Comparison, OpenAI, Buffer, Google Gemini, Llama 3.2 Vision, Anthropic Claude
• outputs: Camera Focus, Single-Label Classification Model, Polygon Zone Visualization, YOLO-World Model, Image Convert Grayscale, Instance Segmentation Model, Trace Visualization, Absolute Static Crop, Perspective Correction, OpenAI, Circle Visualization, Clip Comparison, Image Slicer, OpenAI, Triangle Visualization, Halo Visualization, QR Code Detection, Gaze Detection, Corner Visualization, Object Detection Model, Template Matching, LMM, Roboflow Dataset Upload, VLM as Classifier, Dynamic Crop, Depth Estimation, Buffer, Stitch Images, Segment Anything 2 Model, Object Detection Model, Llama 3.2 Vision, Anthropic Claude, Model Comparison Visualization, Keypoint Detection Model, Crop Visualization, Blur Visualization, CLIP Embedding Model, CogVLM, Dominant Color, Image Threshold, Stability AI Inpainting, SmolVLM2, VLM as Detector, Relative Static Crop, Image Preprocessing, Pixel Color Count, Background Color Visualization, Barcode Detection, Keypoint Visualization, Clip Comparison, Color Visualization, Moondream2, OCR Model, Multi-Label Classification Model, Classification Label Visualization, Google Vision OCR, Pixelate Visualization, Camera Calibration, Image Slicer, Label Visualization, Time in Zone, Reference Path Visualization, Single-Label Classification Model, Google Gemini, Roboflow Dataset Upload, Line Counter Visualization, Qwen2.5-VL, Byte Tracker, Multi-Label Classification Model, Image Blur, Florence-2 Model, SIFT Comparison, Detections Stabilizer, LMM For Classification, Image Contours, Instance Segmentation Model, Polygon Visualization, SIFT, Florence-2 Model, Ellipse Visualization, Mask Visualization, Keypoint Detection Model, Detections Stitch, Bounding Box Visualization, Dot Visualization, Stability AI Image Generation, VLM as Detector, VLM as Classifier

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Grid Visualization in version v1 has.

Bindings

• input

  • images (list_of_values): Images to visualize.
  • width (integer): Width of the output image..
  • height (integer): Height of the output image..

• output

  • image (image): Image in workflows.

Example JSON definition of step Grid Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/grid_visualization@v1",
    "images": "$steps.buffer.output",
    "width": 2560,
    "height": 1440
}