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

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
}