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