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

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

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
}