Identify Outliers

Class: IdentifyOutliersBlockV1

Source: inference.core.workflows.core_steps.sampling.identify_outliers.v1.IdentifyOutliersBlockV1

Identify outlier embeddings compared to prior data.

This block accepts an embedding and compares it to a sample of prior data. If the embedding is an outlier, the block will return a boolean flag and the percentile of the embedding.

Type identifier

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

Properties

Name	Type	Description	Refs
name	str	Unique name of step in workflows.	❌
threshold_percentile	float	The desired sensitivity. A higher value will result in more data points being classified as outliers..	✅
warmup	int	The number of data points to use for the initial average calculation. No outliers are identified during this period..	✅
window_size	int	The number of previous data points to consider in the sliding window algorithm..	✅

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 Identify Outliers in version v1.

• inputs: Detections Consensus, Line Counter, Image Contours, Distance Measurement, Template Matching, CLIP Embedding Model, SIFT Comparison, SIFT Comparison, Identify Changes, Clip Comparison, Identify Outliers, Perspective Correction, Pixel Color Count, Perception Encoder Embedding Model, Line Counter
• outputs: Detections Stitch, Byte Tracker, YOLO-World Model, Dynamic Crop, Blur Visualization, Bounding Box Visualization, Stability AI Outpainting, Identify Changes, Slack Notification, Keypoint Visualization, Trace Visualization, Instance Segmentation Model, Polygon Visualization, Ellipse Visualization, Model Comparison Visualization, Triangle Visualization, Polygon Zone Visualization, SAM 3, Halo Visualization, Byte Tracker, Time in Zone, Stability AI Image Generation, Single-Label Classification Model, Circle Visualization, Email Notification, Detections Stabilizer, Motion Detection, Object Detection Model, Instance Segmentation Model, Perspective Correction, Reference Path Visualization, Twilio SMS/MMS Notification, Color Visualization, Corner Visualization, Multi-Label Classification Model, Image Slicer, Roboflow Custom Metadata, Dot Visualization, Model Monitoring Inference Aggregator, Label Visualization, Background Color Visualization, Time in Zone, Classification Label Visualization, Byte Tracker, Keypoint Detection Model, Roboflow Dataset Upload, Mask Visualization, Dynamic Zone, Detections List Roll-Up, Pixelate Visualization, PTZ Tracking (ONVIF).md), Keypoint Detection Model, Detections Consensus, Webhook Sink, Stability AI Inpainting, Relative Static Crop, Template Matching, Line Counter Visualization, Stitch Images, Crop Visualization, Icon Visualization, SIFT Comparison, Gaze Detection, Identify Outliers, Twilio SMS Notification, Single-Label Classification Model, Time in Zone, SAM 3, Object Detection Model, Multi-Label Classification Model, Segment Anything 2 Model, Image Slicer, Roboflow Dataset Upload, Email Notification

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Identify Outliers in version v1 has.

Bindings

• input

  • embedding (embedding): Embedding of the current data..
  • threshold_percentile (float_zero_to_one): The desired sensitivity. A higher value will result in more data points being classified as outliers..
  • warmup (integer): The number of data points to use for the initial average calculation. No outliers are identified during this period..
  • window_size (integer): The number of previous data points to consider in the sliding window algorithm..

• output

  • is_outlier (boolean): Boolean flag.
  • percentile (float_zero_to_one): float value in range [0.0, 1.0].
  • warming_up (boolean): Boolean flag.

Example JSON definition of step Identify Outliers in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/identify_outliers@v1",
    "embedding": "$steps.clip.embedding",
    "threshold_percentile": "$inputs.sample_rate",
    "warmup": 100,
    "window_size": 5
}