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