Gaze Detection

Class: GazeBlockV1

Source: inference.core.workflows.core_steps.models.foundation.gaze.v1.GazeBlockV1

Run L2CS Gaze detection model on faces in images.

This block can: 1. Detect faces in images and estimate their gaze direction 2. Estimate gaze direction on pre-cropped face images

The gaze direction is represented by yaw and pitch angles in degrees.

Type identifier

Use the following identifier in step "type" field: roboflow_core/gaze@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
do_run_face_detection	bool	Whether to run face detection. Set to False if input images are pre-cropped face images..	✅

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 Gaze Detection in version v1.

• inputs: Crop Visualization, SIFT, Stability AI Image Generation, Triangle Visualization, Blur Visualization, PTZ Tracking (ONVIF).md), Background Color Visualization, Relative Static Crop, Color Visualization, Image Contours, Camera Focus, Corner Visualization, Line Counter Visualization, Slack Notification, Icon Visualization, Mask Visualization, Image Convert Grayscale, Circle Visualization, Image Blur, Pixelate Visualization, SIFT Comparison, Absolute Static Crop, Model Comparison Visualization, Webhook Sink, VLM as Detector, VLM as Classifier, Dynamic Zone, Image Threshold, VLM as Classifier, Reference Path Visualization, Detections Consensus, Image Slicer, Roboflow Dataset Upload, Stitch Images, Identify Outliers, Depth Estimation, Roboflow Dataset Upload, Trace Visualization, Image Preprocessing, Classification Label Visualization, Polygon Visualization, Roboflow Custom Metadata, Stability AI Outpainting, Keypoint Visualization, Dot Visualization, Email Notification, Grid Visualization, Local File Sink, JSON Parser, Bounding Box Visualization, Camera Calibration, Polygon Zone Visualization, Ellipse Visualization, QR Code Generator, Halo Visualization, Perspective Correction, Stability AI Inpainting, Model Monitoring Inference Aggregator, Image Slicer, Twilio SMS Notification, SIFT Comparison, Label Visualization, Identify Changes, VLM as Detector, Dynamic Crop
• outputs: Anthropic Claude, Segment Anything 2 Model, Crop Visualization, Triangle Visualization, Blur Visualization, PTZ Tracking (ONVIF).md), Background Color Visualization, Color Visualization, Line Counter Visualization, Corner Visualization, Icon Visualization, Pixelate Visualization, Circle Visualization, Google Gemini, Webhook Sink, Model Comparison Visualization, Llama 3.2 Vision, Dynamic Zone, Detections Consensus, Roboflow Dataset Upload, Byte Tracker, Roboflow Dataset Upload, Detection Offset, Detections Filter, Trace Visualization, OpenAI, Roboflow Custom Metadata, Keypoint Visualization, Dot Visualization, Detections Transformation, Bounding Box Visualization, Camera Calibration, Detections Classes Replacement, Ellipse Visualization, Detections Merge, Florence-2 Model, Florence-2 Model, OpenAI, Model Monitoring Inference Aggregator, Template Matching, Velocity, Label Visualization, Distance Measurement, Dynamic Crop

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Gaze Detection in version v1 has.

Bindings

• input

  • images (image): The image to infer on..
  • do_run_face_detection (boolean): Whether to run face detection. Set to False if input images are pre-cropped face images..

• output

  • face_predictions (keypoint_detection_prediction): Prediction with detected bounding boxes and detected keypoints in form of sv.Detections(...) object.
  • yaw_degrees (float): Float value.
  • pitch_degrees (float): Float value.

Example JSON definition of step Gaze Detection in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/gaze@v1",
    "images": "$inputs.image",
    "do_run_face_detection": "<block_does_not_provide_example>"
}