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

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
}