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@v1
to 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
,Pixel Color Count
,CLIP Embedding Model
,Clip Comparison
,Line Counter
,Detections Consensus
,SIFT Comparison
,Distance Measurement
,SIFT Comparison
,Identify Outliers
,Perspective Correction
,Image Contours
,Identify Changes
,Line Counter
- outputs:
Classification Label Visualization
,Camera Calibration
,Instance Segmentation Model
,Stitch Images
,Cosine Similarity
,Byte Tracker
,Image Slicer
,Roboflow Dataset Upload
,Distance Measurement
,Multi-Label Classification Model
,Twilio SMS Notification
,Object Detection Model
,Byte Tracker
,OpenAI
,Label Visualization
,Single-Label Classification Model
,Roboflow Dataset Upload
,Detections Stitch
,Bounding Box Visualization
,Llama 3.2 Vision
,Model Comparison Visualization
,Slack Notification
,Object Detection Model
,Detections Consensus
,Dynamic Zone
,Halo Visualization
,Triangle Visualization
,Time in Zone
,Keypoint Detection Model
,Model Monitoring Inference Aggregator
,Reference Path Visualization
,Perspective Correction
,Time in Zone
,Dynamic Crop
,Identify Changes
,Stability AI Image Generation
,Ellipse Visualization
,Blur Visualization
,Dot Visualization
,Circle Visualization
,Background Color Visualization
,Gaze Detection
,Multi-Label Classification Model
,Single-Label Classification Model
,Color Visualization
,Byte Tracker
,Pixelate Visualization
,Identify Outliers
,Google Gemini
,Keypoint Detection Model
,Polygon Zone Visualization
,Relative Static Crop
,Detections Stabilizer
,Keypoint Visualization
,Template Matching
,Mask Visualization
,Velocity
,Line Counter Visualization
,Roboflow Custom Metadata
,Anthropic Claude
,Webhook Sink
,SIFT Comparison
,YOLO-World Model
,Trace Visualization
,Instance Segmentation Model
,Corner Visualization
,Polygon Visualization
,Segment Anything 2 Model
,Crop Visualization
,Email Notification
,Image Slicer
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
}