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