Box Prompting (AI Labeling)
Annotate images with our AI Labeling tool that improves with each example.
Last updated
Annotate images with our AI Labeling tool that improves with each example.
Last updated
Box Prompting takes one (or more) prompt bounding boxes to generate annotations for similar objects. Each example fine-tunes a model that improves with each image. With Box Prompting, you save hours of time manually drawing bounding boxes around objects that appear multiple times in a dataset.
Box prompting requires you to create at least one bounding box annotation to provide as an example for generating predictions.
Any additional annotations you create with other labels will help the model differentiate between different objects in the image. For best results, provide 1-2 examples of every unique object in your images.
You will see predictions from the server appear - adjust the confidence threshold using the slider to adjust the number of predictions displayed.
Click on a prediction to either save a new annotation from the prediction or delete it individually. If you are satisfied with all of the predictions, click "Approve All Predictions".
On images that contain several objects with similar appearances, it can be helpful to provide at least one example for each significant color, size or camera angle variation.
Box prompting works best when your images have similar contents, allowing you to quickly reuse your training examples while generating predictions.
Often, the predicted bounding box is larger than it should be - reduce the size to avoid erroneously including parts of the background.
Although we can provide predictions for documents or computer graphics, Box Prompting works best for identifying repetitive items in photos.
If you notice a particular annotation class produces false positive predictions, you can add _negative to the end of the label to provide a negative example to the box promp model.