Identify Outliers¶
Class: IdentifyOutliersBlockV1
Source: inference.core.workflows.core_steps.sampling.identify_outliers.v1.IdentifyOutliersBlockV1
Identify outlier embeddings compared to prior data.
This block accepts an embedding and compares it to a sample of prior data. If the embedding is an outlier, the block will return a boolean flag and the percentile of the embedding.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/identify_outliers@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Unique name of step in workflows. | ❌ |
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.. | ✅ |
window_size |
int |
The number of previous 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 Outliers 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:
Triangle Visualization,Mask Visualization,Instance Segmentation Model,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,Crop Visualization,Line Counter Visualization,Ellipse Visualization,Twilio SMS Notification,Identify Changes,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,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,Corner Visualization,Color Visualization,Roboflow Custom Metadata,Email Notification,Slack Notification,Polygon Visualization,Bounding Box Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Identify Outliers 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..window_size(integer): The number of previous 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].warming_up(boolean): Boolean flag.
Example JSON definition of step Identify Outliers in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/identify_outliers@v1",
"embedding": "$steps.clip.embedding",
"threshold_percentile": "$inputs.sample_rate",
"warmup": 100,
"window_size": 5
}