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

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"
}