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

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
}