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

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"
}