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

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

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
}