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

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
}