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

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
}