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