SIFT

Class: SIFTBlockV1

Source: inference.core.workflows.core_steps.classical_cv.sift.v1.SIFTBlockV1

The Scale-Invariant Feature Transform (SIFT) algorithm is a popular method in computer vision for detecting and describing features (interesting parts) in images. SIFT is used to find key points in an image and describe them in a way that allows for recognizing the same objects or features in different images, even if the images are taken from different angles, distances, or lighting conditions.

Read more: https://en.wikipedia.org/wiki/Scale-invariant_feature_transform

Type identifier

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

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌

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 SIFT in version v1.

• inputs: Label Visualization, Blur Visualization, Background Color Visualization, Contrast Equalization, Bounding Box Visualization, Camera Calibration, Polygon Visualization, Stability AI Outpainting, Image Slicer, Keypoint Visualization, Reference Path Visualization, Pixelate Visualization, Icon Visualization, Triangle Visualization, Model Comparison Visualization, Corner Visualization, Image Preprocessing, Color Visualization, SIFT Comparison, Line Counter Visualization, Grid Visualization, Stitch Images, Halo Visualization, Stability AI Image Generation, QR Code Generator, Circle Visualization, Image Contours, Relative Static Crop, Dot Visualization, Polygon Zone Visualization, Ellipse Visualization, Image Blur, Absolute Static Crop, Depth Estimation, Image Slicer, Morphological Transformation, Stability AI Inpainting, Dynamic Crop, Camera Focus, Crop Visualization, Image Threshold, Perspective Correction, Image Convert Grayscale, Mask Visualization, Trace Visualization, Classification Label Visualization, SIFT
• outputs: Google Vision OCR, Label Visualization, LMM For Classification, Blur Visualization, Background Color Visualization, Contrast Equalization, Reference Path Visualization, Keypoint Visualization, Stability AI Outpainting, Bounding Box Visualization, Image Slicer, Pixelate Visualization, Single-Label Classification Model, Clip Comparison, SAM 3, Perception Encoder Embedding Model, Seg Preview, Byte Tracker, Image Preprocessing, SAM 3, Color Visualization, SIFT Comparison, Qwen2.5-VL, Object Detection Model, Dominant Color, Anthropic Claude, Circle Visualization, Image Contours, Object Detection Model, QR Code Detection, Polygon Zone Visualization, Ellipse Visualization, Email Notification, Clip Comparison, Moondream2, VLM as Classifier, OCR Model, Absolute Static Crop, Depth Estimation, LMM, Time in Zone, Morphological Transformation, Roboflow Dataset Upload, Gaze Detection, Crop Visualization, OpenAI, Florence-2 Model, Barcode Detection, Image Convert Grayscale, SAM 3, CogVLM, VLM as Detector, Multi-Label Classification Model, Classification Label Visualization, Buffer, Keypoint Detection Model, Segment Anything 2 Model, Keypoint Detection Model, SIFT Comparison, YOLO-World Model, Polygon Visualization, CLIP Embedding Model, Camera Calibration, Icon Visualization, Triangle Visualization, Template Matching, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Corner Visualization, Florence-2 Model, Google Gemini, Google Gemini, EasyOCR, VLM as Detector, Line Counter Visualization, SmolVLM2, Halo Visualization, Stability AI Image Generation, Relative Static Crop, Dot Visualization, Detections Stitch, Llama 3.2 Vision, Image Blur, OpenAI, Instance Segmentation Model, Multi-Label Classification Model, Image Slicer, OpenAI, Stability AI Inpainting, Dynamic Crop, Single-Label Classification Model, Camera Focus, Pixel Color Count, Detections Stabilizer, Instance Segmentation Model, VLM as Classifier, Mask Visualization, Perspective Correction, Image Threshold, OpenAI, Trace Visualization, Stitch Images, SIFT

Input and Output Bindings

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

Bindings

• input

  • image (image): The input image for this step..

• output

  • image (image): Image in workflows.
  • keypoints (image_keypoints): Image keypoints detected by classical Computer Vision method.
  • descriptors (numpy_array): Numpy array.

Example JSON definition of step SIFT in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/sift@v1",
    "image": "$inputs.image"
}