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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
Image Captioning

If you want to train a machine learning model to caption or add alt text to images, use this template to collect captions about images.
Interactive Template Preview
Labeling Configuration
<View>
<Image name="image" value="$captioning"/>
<Header value="Describe the image:"/>
<TextArea name="caption" toName="image" placeholder="Enter description here..." rows="5" maxSubmissions="1"/>
</View>
About the labeling configuration
All labeling configurations must be wrapped in View tags.
Use the Image object tag to specify the image to caption:
<Image name="image" value="$captioning"/>
You can add a header to provide instructions to the annotator:
<Header value="Describe the image:"/>
Use the TextArea control tag to provide a 5 row text box that annotators can type a caption into:
<TextArea name="caption" toName="image" placeholder="Enter description here..." rows="5" maxSubmissions="1"/>
Use the placeholder
argument to provide placeholder text to the annotator, which can provide an example or further instructions.
Enhance this template
You can enhance this template in many ways.
Add a sticky header so that you can always view the caption
If you want to always view the annotation options on the labeling interface, even if you need to scroll around on the image or data being labeled, you can use styling with the View tag to specify the position.
In this case, wrap the caption element in styled View tags:
<View style="padding: 0 1em; margin: 1em 0; background: #f1f1f1; position: sticky; top: 0; border-radius: 3px">
<TextArea name="caption" toName="image" placeholder="Enter description here..." rows="5" maxSubmissions="1"/>
</View>
The position: sticky; top: 0;
CSS sets the TextArea to be fixed at the top of the screen after the annotator scrolls down when viewing the task. The other styling options visually differentiate the section of the interface containing the text box from the rest of the interface.
Related tags

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