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