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

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
}