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

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
}