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