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Computer Vision
- Semantic Segmentation with Polygons
- Semantic Segmentation with Masks
- Object Detection with Bounding Boxes
- Keypoint Labeling
- Image Captioning
- Optical Character Recognition (OCR)
- Image Classification
- Visual Question Answering
- Object Detection with Ellipses
- Multi-Image Classification
- Multi-page Document Annotation
- Inventory Tracking
- Visual Genome
Natural Language Processing
- Question Answering
- Sentiment Analysis Text Classification
- Named Entity Recognition
- Taxonomy
- Relation Extraction
- Text Summarization
- Machine Translation
Audio/Speech Processing
- Automatic Speech Recognition
- Sound Event Detection
- Automatic Speech Recognition using Segments
- Signal Quality Detection
- Speaker Diarization
- Dialogue Analysis
- Intent Classification
- Audio Classification
- Audio Classification with Segments
- Voice Activity Detection
Conversational AI
- Response Generation
- Response Selection
- Coreference Resolution and Entity Linking
- Slot Filling and Intent Classification
Ranking and Scoring
- Pairwise Regression
- Document Retrieval
- Pairwise Classification
- Content-based Image Retrieval
- Website Rating
- ASR Hypotheses Selection
- Text-to-Image Generation
- Search Page Ranking
Structured Data Parsing
- Freeform Metadata
- PDF Classification
- Tabular Data
- HTML Entity Recognition
- HTML Classification
Time Series Analysis
- Time Series Forecasting
- Change Point Detection
- Activity Recognition
- Signal Quality
- Outliers and Anomaly Detection
- Time Series Classification
- Time Series Labeling
Videos
- Video Classification
- Video Timeline Segmentation
- Video Object Detection and Tracking
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- Template Gallery - Computer Vision
- Template Gallery - Natural Language Processing
- Template Gallery - Audio/Speech Processing
- Template Gallery - Conversational AI
- Template Gallery - Ranking & Scoring
- Template Gallery - Structured Data Parsing
- Template Gallery - Time Series Analysis
- Template Gallery - Videos
- Template Gallery - Dynamic Labels
Pairwise Regression

If you need a dataset to train a pairwise regression model, use this template to rate pairs of images based on how similar they are. You can also customize this template to rate different properties of different types of data, such as rating whether the sentiment of two text excerpts of movie reviews is similar.
Interactive Template Preview
Labeling Configuration
<View>
<Header>Set how likely these images represent the same thing:</Header>
<View style="display: grid; column-gap: 8px; grid-template: auto/1fr 1fr">
<Image name="image1" value="$image1" />
<Image name="image2" value="$image2" />
</View>
<View style="margin-left: auto; margin-right: auto; width: 16em">
<Rating name="rating" toName="image1,image2"/>
</View>
</View>
About the labeling configuration
All labeling configurations must be wrapped in View tags.
You can add a header to provide instructions to the annotator:
<Header>Set how likely these images represent the same thing:</Header>
Add styling to a View tag that wraps the Image tags to control how the image data is displayed:
<View style="display: grid; column-gap: 8px; grid-template: auto/1fr 1fr">
This styling displays the enclosed items in a grid with a gap of 8 pixels between columns.
Use the Image object tag to specify two images on the labeling interface:
<Image name="image1" value="$image1" />
<Image name="image2" value="$image2" />
Close the View tag after the Image tags.
Add a new View tag with styling to control how the rating option is displayed on the labeling interface:
<View style="margin-left: auto; margin-right: auto; width: 16em">
Use the Rating control tag to display star ratings to apply to both images:
<Rating name="rating" toName="image1,image2"/>
Close the View tag after the Rating tag.
Related tags

If you found an error, you can file an issue on GitHub!