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