Identify Outliers¶
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¶
Check what blocks you can connect to Identify Outliers in version v1.
- inputs:
Detections Consensus,Line Counter,Image Contours,Pixel Color Count,CLIP Embedding Model,SIFT Comparison,Identify Outliers,Template Matching,Clip Comparison,Distance Measurement,SIFT Comparison,Line Counter,Identify Changes - outputs:
Multi-Label Classification Model,Reference Path Visualization,Single-Label Classification Model,Roboflow Dataset Upload,Trace Visualization,Halo Visualization,Byte Tracker,Instance Segmentation Model,SIFT Comparison,Pixelate Visualization,Email Notification,Classification Label Visualization,Mask Visualization,Background Color Visualization,Crop Visualization,Relative Static Crop,Gaze Detection,Byte Tracker,Roboflow Custom Metadata,Bounding Box Visualization,Keypoint Detection Model,Multi-Label Classification Model,YOLO-World Model,Instance Segmentation Model,Template Matching,Byte Tracker,Object Detection Model,Roboflow Dataset Upload,Identify Changes,Keypoint Visualization,Polygon Zone Visualization,Model Monitoring Inference Aggregator,Object Detection Model,Triangle Visualization,Single-Label Classification Model,Ellipse Visualization,Detections Stitch,Identify Outliers,Slack Notification,Twilio SMS Notification,Dot Visualization,Model Comparison Visualization,Dynamic Crop,Image Slicer,Perspective Correction,Line Counter Visualization,Detections Consensus,Time in Zone,Webhook Sink,Time in Zone,Stitch Images,Blur Visualization,Keypoint Detection Model,Color Visualization,Polygon Visualization,Detections Stabilizer,Segment Anything 2 Model,Circle Visualization,Label Visualization,Corner 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
}