Identify Changes

Class: IdentifyChangesBlockV1

Source: inference.core.workflows.core_steps.sampling.identify_changes.v1.IdentifyChangesBlockV1

Identify changes compared to prior data via embeddings.

This block accepts an embedding and compares it to a prior average and standard deviation for the rate of change. When things change faster or slower than they have in the past, the block will flag the data as an outlier.

Type identifier

Use the following identifier in step "type" field: roboflow_core/identify_changes@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Unique name of step in workflows.	❌
strategy	str	The change identification algorithm to use..	❌
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..	✅
smoothing_factor	float	The smoothing factor for the EMA algorithm. The default of 0.25 means the most recent data point will carry 25% weight in the average. Higher values will make the average more responsive to recent data points..	✅
window_size	int	The number of 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 Changes in version v1.

• inputs: Identify Outliers, Clip Comparison, Line Counter, Image Contours, Detections Consensus, Distance Measurement, Template Matching, Line Counter, SIFT Comparison, CLIP Embedding Model, Pixel Color Count, SIFT Comparison, Identify Changes
• outputs: Multi-Label Classification Model, Relative Static Crop, Triangle Visualization, OpenAI, Keypoint Detection Model, Polygon Zone Visualization, Keypoint Visualization, Detections Consensus, Distance Measurement, Model Comparison Visualization, Instance Segmentation Model, Anthropic Claude, Multi-Label Classification Model, YOLO-World Model, Roboflow Dataset Upload, Detections Stabilizer, SIFT Comparison, Dot Visualization, Roboflow Dataset Upload, Color Visualization, Email Notification, Google Gemini, Mask Visualization, Detections Stitch, Image Slicer, Bounding Box Visualization, Time in Zone, Classification Label Visualization, Halo Visualization, Stitch Images, Velocity, Line Counter Visualization, Segment Anything 2 Model, Blur Visualization, Roboflow Custom Metadata, Identify Outliers, Byte Tracker, Perspective Correction, Slack Notification, Model Monitoring Inference Aggregator, Corner Visualization, Label Visualization, Llama 3.2 Vision, Dynamic Crop, Circle Visualization, Instance Segmentation Model, Camera Calibration, Single-Label Classification Model, Stability AI Image Generation, Webhook Sink, Image Slicer, Identify Changes, Object Detection Model, Time in Zone, Background Color Visualization, Polygon Visualization, Cosine Similarity, Gaze Detection, Dynamic Zone, Byte Tracker, Single-Label Classification Model, Trace Visualization, Keypoint Detection Model, Reference Path Visualization, Template Matching, Byte Tracker, Object Detection Model, Crop Visualization, Ellipse Visualization, Twilio SMS Notification, Pixelate Visualization

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Identify Changes 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..
  • smoothing_factor (float_zero_to_one): The smoothing factor for the EMA algorithm. The default of 0.25 means the most recent data point will carry 25% weight in the average. Higher values will make the average more responsive to recent data points..
  • window_size (integer): The number of 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].
  • z_score (float): Float value.
  • average (embedding): A list of floating point numbers representing a vector embedding..
  • std (embedding): A list of floating point numbers representing a vector embedding..
  • warming_up (boolean): Boolean flag.

Example JSON definition of step Identify Changes in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/identify_changes@v1",
    "strategy": "Simple Moving Average (SMA)",
    "embedding": "$steps.clip.embedding",
    "threshold_percentile": "$inputs.sample_rate",
    "warmup": 100,
    "smoothing_factor": 0.1,
    "window_size": 5
}