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

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
}