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

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"
}