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@v1
to 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:
SIFT Comparison
,Line Counter
,Template Matching
,CLIP Embedding Model
,Line Counter
,SIFT Comparison
,Distance Measurement
,Identify Outliers
,Clip Comparison
,Perception Encoder Embedding Model
,Image Contours
,Identify Changes
,Perspective Correction
,Pixel Color Count
,Detections Consensus
- outputs:
Image Slicer
,Instance Segmentation Model
,Dynamic Crop
,Time in Zone
,Multi-Label Classification Model
,Roboflow Dataset Upload
,Color Visualization
,Corner Visualization
,Keypoint Detection Model
,Keypoint Visualization
,Stability AI Outpainting
,PTZ Tracking (ONVIF)
.md),Trace Visualization
,Keypoint Detection Model
,Email Notification
,Time in Zone
,Model Comparison Visualization
,Single-Label Classification Model
,YOLO-World Model
,Mask Visualization
,Model Monitoring Inference Aggregator
,Image Slicer
,Multi-Label Classification Model
,Detections Consensus
,Classification Label Visualization
,Time in Zone
,Relative Static Crop
,Dynamic Zone
,Blur Visualization
,Stitch Images
,Roboflow Dataset Upload
,Triangle Visualization
,Perspective Correction
,Icon Visualization
,Object Detection Model
,Label Visualization
,Stability AI Image Generation
,Ellipse Visualization
,Detections Stabilizer
,Byte Tracker
,Single-Label Classification Model
,Line Counter Visualization
,Slack Notification
,SIFT Comparison
,Byte Tracker
,Roboflow Custom Metadata
,Gaze Detection
,Twilio SMS Notification
,Background Color Visualization
,Segment Anything 2 Model
,Identify Changes
,Polygon Zone Visualization
,Detections Stitch
,Byte Tracker
,Polygon Visualization
,Dot Visualization
,Template Matching
,Instance Segmentation Model
,Identify Outliers
,Circle Visualization
,Bounding Box Visualization
,Object Detection Model
,Halo Visualization
,Reference Path Visualization
,Pixelate Visualization
,Webhook Sink
,Stability AI Inpainting
,Crop 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
):float
value 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
}