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