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

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
}