Image Slicer

v2

Class: ImageSlicerBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.transformations.image_slicer.v2.ImageSlicerBlockV2

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

This block enables Slicing Adaptive Inference (SAHI) technique in Workflows providing implementation for first step of procedure - making slices out of input image.

To use the block effectively, it must be paired with detection model (object-detection or instance segmentation) running against output images from this block. At the end - Detections Stitch block must be applied on top of predictions to merge them as if the prediction was made against input image, not its slices.

We recommend adjusting the size of slices to match the model's input size and the scale of objects in the dataset the model was trained on. Models generally perform best on data that is similar to what they encountered during training. The default size of slices is 640, but this might not be optimal if the model's input size is 320, as each slice would be downsized by a factor of two during inference. Similarly, if the model's input size is 1280, each slice will be artificially up-scaled. The best setup should be determined experimentally based on the specific data and model you are using.

To learn more about SAHI please visit Roboflow blog which describes the technique in details, yet not in context of Roboflow workflows.

Changes compared to v1

• All crops generated by slicer will be of equal size

• No duplicated crops will be created

Type identifier

Use the following identifier in step "type" field: roboflow_core/image_slicer@v2to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
slice_width	int	Width of each slice, in pixels.	✅
slice_height	int	Height of each slice, in pixels.	✅
overlap_ratio_width	float	Overlap ratio between consecutive slices in the width dimension.	✅
overlap_ratio_height	float	Overlap ratio between consecutive slices in the height dimension.	✅

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 Image Slicer in version v2.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Image Slicer in version v2 has.

Bindings

• input

  • image (image): The input image for this step..
  • slice_width (integer): Width of each slice, in pixels.
  • slice_height (integer): Height of each slice, in pixels.
  • overlap_ratio_width (float_zero_to_one): Overlap ratio between consecutive slices in the width dimension.
  • overlap_ratio_height (float_zero_to_one): Overlap ratio between consecutive slices in the height dimension.

• output

  • slices (image): Image in workflows.

Example JSON definition of step Image Slicer in version v2

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/image_slicer@v2",
    "image": "$inputs.image",
    "slice_width": 320,
    "slice_height": 320,
    "overlap_ratio_width": 0.2,
    "overlap_ratio_height": 0.2
}

v1

Class: ImageSlicerBlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.transformations.image_slicer.v1.ImageSlicerBlockV1

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

This block enables Slicing Adaptive Inference (SAHI) technique in Workflows providing implementation for first step of procedure - making slices out of input image.

To use the block effectively, it must be paired with detection model (object-detection or instance segmentation) running against output images from this block. At the end - Detections Stitch block must be applied on top of predictions to merge them as if the prediction was made against input image, not its slices.

We recommend adjusting the size of slices to match the model's input size and the scale of objects in the dataset the model was trained on. Models generally perform best on data that is similar to what they encountered during training. The default size of slices is 640, but this might not be optimal if the model's input size is 320, as each slice would be downsized by a factor of two during inference. Similarly, if the model's input size is 1280, each slice will be artificially up-scaled. The best setup should be determined experimentally based on the specific data and model you are using.

To learn more about SAHI please visit Roboflow blog which describes the technique in details, yet not in context of Roboflow workflows.

Type identifier

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

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
slice_width	int	Width of each slice, in pixels.	✅
slice_height	int	Height of each slice, in pixels.	✅
overlap_ratio_width	float	Overlap ratio between consecutive slices in the width dimension.	✅
overlap_ratio_height	float	Overlap ratio between consecutive slices in the height dimension.	✅

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 Image Slicer in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Image Slicer in version v1 has.

Bindings

• input

  • image (image): The input image for this step..
  • slice_width (integer): Width of each slice, in pixels.
  • slice_height (integer): Height of each slice, in pixels.
  • overlap_ratio_width (float_zero_to_one): Overlap ratio between consecutive slices in the width dimension.
  • overlap_ratio_height (float_zero_to_one): Overlap ratio between consecutive slices in the height dimension.

• output

  • slices (image): Image in workflows.

Example JSON definition of step Image Slicer in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/image_slicer@v1",
    "image": "$inputs.image",
    "slice_width": 320,
    "slice_height": 320,
    "overlap_ratio_width": 0.2,
    "overlap_ratio_height": 0.2
}