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

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"
}