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

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"
}