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.
Click "Predict" to see the magic happen! Box Prompting will generate predictions based on your initial annotations. Predictions will appear with dotted lines.
Predictions are not annotations and will not be saved when navigating away from the image. See Step 4 for how to save your predictions.
From here, you can:
Adjust the confidence threshold using the slider to adjust the number of predictions displayed. Higher confidence means less predictions.
If any incorrect predictions occur, you can right click on the box and select "Convert to Negative". This will teach the model to not label this type of object in the future. Negative examples will appear shaded in.
You can also convert existing annotations to negative through the same right click menu.
Any additional annotations you create with other labels will help the model differentiate between different objects in the image. After adding more examples, you can click "Predict" to generate new predictions.
For best results, provide 1-2 examples of every unique object in your images.
You may find it easier to fine-tune predictions by lowering the confidence & converting excess predictions to negative, rather than setting the confidence high.
Once the predictions are to your liking, click "Approve Predictions". This will convert all predictions to annotations, and ensure they'll be saved if you navigate away.
From here, you can edit & delete annotations as usual.
As you annotate images, Box Prompting will be trained on any images with human-drawn or human-edited annotations. (Predictions that are approved without edits will not be included.)
This means you can click "Predict" on new images without drawing a single box & still generate predictions! You can check the number of images included in the training set in the tool menu.
Once the model has a large enough training set and is performing well, you can turn on "Auto-Run". This will automatically generate & approve predictions on new images, so all you have to do is move to the next image.
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 right click & select "Convert to Negative" to provide a negative example to the Box Prompting model.
