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

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
}