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

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
}