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