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@v1
to 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:
Stability AI Inpainting
,Identify Changes
,Model Monitoring Inference Aggregator
,Label Visualization
,Depth Estimation
,Corner Visualization
,Triangle Visualization
,Background Color Visualization
,Image Blur
,Polygon Zone Visualization
,Model Comparison Visualization
,Line Counter Visualization
,Camera Focus
,Circle Visualization
,Perspective Correction
,Relative Static Crop
,Grid Visualization
,Stability AI Image Generation
,VLM as Detector
,Trace Visualization
,Image Slicer
,Blur Visualization
,Dynamic Zone
,Classification Label Visualization
,Image Convert Grayscale
,Image Preprocessing
,Slack Notification
,SIFT Comparison
,Detections Consensus
,Stitch Images
,Reference Path Visualization
,Stability AI Outpainting
,Polygon Visualization
,Webhook Sink
,Roboflow Dataset Upload
,Camera Calibration
,Identify Outliers
,Mask Visualization
,SIFT
,Bounding Box Visualization
,Image Threshold
,Roboflow Custom Metadata
,Local File Sink
,Keypoint Visualization
,Ellipse Visualization
,Crop Visualization
,Color Visualization
,Pixelate Visualization
,Image Slicer
,VLM as Classifier
,Dynamic Crop
,Image Contours
,JSON Parser
,Absolute Static Crop
,Twilio SMS Notification
,Email Notification
,PTZ Tracking (ONVIF)
.md),Halo Visualization
,SIFT Comparison
,Dot Visualization
,VLM as Detector
,Roboflow Dataset Upload
,VLM as Classifier
- outputs:
Florence-2 Model
,Template Matching
,Model Monitoring Inference Aggregator
,Label Visualization
,Florence-2 Model
,Corner Visualization
,Triangle Visualization
,Background Color Visualization
,Model Comparison Visualization
,Detections Transformation
,Line Counter Visualization
,Circle Visualization
,Trace Visualization
,Blur Visualization
,Dynamic Zone
,Byte Tracker
,Detections Consensus
,Detection Offset
,OpenAI
,Velocity
,Llama 3.2 Vision
,Detections Filter
,Anthropic Claude
,Webhook Sink
,Roboflow Dataset Upload
,Segment Anything 2 Model
,Detections Merge
,Camera Calibration
,Roboflow Custom Metadata
,Bounding Box Visualization
,Distance Measurement
,Keypoint Visualization
,Ellipse Visualization
,Crop Visualization
,Color Visualization
,Pixelate Visualization
,Google Gemini
,Dynamic Crop
,Detections Classes Replacement
,PTZ Tracking (ONVIF)
.md),OpenAI
,Dot Visualization
,Roboflow Dataset Upload
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Gaze Detection
in version v1
has.
Bindings
-
input
-
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>"
}