9 Essential Features for a Bounding Box Annotation Tool

Copy-paste, and other power user shortcuts

Got a few similar objects to annotate using bounding boxes?

Copy-pasting your boxes can be very handy at speeding up your annotation process. It also ensures that your annotations are consistent for the same objects located in different areas of an image or a video.

Are hotkeys a priority in your annotation tool?

Your labeling team should attempt to turn everyone into a power user. Keyboard shortcuts are a good way to get more training data and less fatigue (which leads to some of the hardest training data errors to spot).

Shortcuts to consider are switching classes, cycling between boxes, or points  in a box. V7 also offers keyboard shortcuts for moving annotations and moving  individual points of boxes.

Some projects might require you to copy all your annotations from one image to  another. It often happens when your dataset images are sequential.

We added a button on V7 to carry over annotations from one image to another.

Here are things to look out for in power user shortcuts:

• Can you copy/paste boxes?

• Can you only paste attributes from one box to another?  

• Can you switch between any class without needing to switch from keyboard to mouse?  

• Can you cycle between bounding boxes to quickly perform a quality review    using a single key and camera controls?  

• Can you edit the size and position of boxes using your keyboard?

We’ve added a handy list of shortcuts on every page and append each next to a  button to encourage learning them while using the platform with a mouse.

Bounding boxes attributes and other sub-annotations

Attributes are simply annotation tags that can define the specific features of a given object.

Many object detection projects require labelers to add label attributes on top of the bounding box annotation—it helps describe a given object in greater detail.

For example, it’s common to add label attributes such as occluded, truncated, and crowded, indicating that annotated objects  are in close relationship with other objects in the image.

V7 allows you to add attributes to your bounding box annotations. You can annotate an image and add as many tags as you need to describe an object on a much more granular level.

 We also included the ability to add other sub-annotation types, such as free text, directional vector, a custom ID (used in object re-identification,  multi-camera setups, or other edge cases), and there are many more to come.

Here are things to watch out for in sub annotations: 

• Can you add attributes to bounding boxes?

• Are attributes re-usable in other classes (in other words, do they become    entries as part of that class right away?)  

• Can attributes be inserted easily and is there keyboard shortcut support for them? (attribute application accounts for >50% of most bounding box    labeling projects)  

• Are other sub-annotation types similarly designed to support shortcuts?  

• Can you easily remove attributes during the review?

• Can you edit them once they are created, and do these changes propagate to the whole dataset?

Note: A few annotation tools have  dataset management capabilities, which means that if you make a change to an attribute or class name after  creating it, you might have to go and propagate the change to every annotation  file using a script to avoid breaking changes.

This can be incredibly frustrating, so it's always best to invest in a dataset management solution before you start any labeling project.

Visibility options for bounding boxes

Drawing and editing boxes are one part of the challenge.

But—

How easily can you see them and the image below them?

Z-stack

V7 was built to have every annotation with an editable Z-value, You simply  have to drag an annotation to reorder it.

The same can be done in the video timeline, with an option to automatically  adjust this order to save vertical space.

This one is especially useful when you have hundreds of annotations—such as in  sports analytics.

Image Manipulation Options

You can also adjust the box opacity, border opacity, and visual features of  the image.

V7 also has windowing and color map options, which allow you to see elements  of the image not visible by the naked eye in regular RGB monitors.

In the example above, this x-ray has over 6,000 units of greyscale color per pixel, whilst our monitors can only display 255.

Check out Medical Image Annotation using V7.

How many annotations can it handle?

Most JavaScript libraries aren’t made to handle the scale that AI projects bring to the table.

Make sure that your tool is tested for performance when hundreds of bounding boxes enter the scene. This is especially important in videos where annotations must be kept in memory to ensure smooth playback.

At V7 we established our maximum at 10,000 per image. It’s the highest in the market by an order of magnitude. The same goes for polygons and other annotation types.

 The GIF below shows over 500 polygons with over 50 coordinate points each.

You can technically add more than 10,000 annotations per frame on V7 but will  start seeing performance issues unless your machine is top-notch.

Here are things to consider:

• How many boxes per image can the tool handle?

• What happens if the boxes all contain attributes, does the limit change?  

• Have you tested the platform on a video or a high-resolution image when there are hundreds of boxes in the scene?  

• What features start to perform poorly when images become very busy?

Ready to train your models? Have a look at  Mean Average Precision (mAP) Explained: Everything You Need to Know.

Converting other annotations to Bounding Boxes

Some annotation formats such as COCO expect a bounding box to be around each  polygon. Models like Mask R-CNN also benefit from this detector/segmenter  approach.

We give you this option out of the box.

Since it’s easy to make polygons on V7 using Auto-Annotate, you can export these as bounding boxes.

 Moreover, you won’t have to make a box “around” a polygon, you can simply draw a polygon and use its “free” surrounding box to train a detector.

API functionalities and common bugs to watch out for

 Ultimately, nothing can be more dangerous than a tool you commit to and encounter breaking bugs in its API halfway through your project.

 Here are the most common bugs, or feature failures that we’ve encountered  across  image annotation tools, in order of frequency:

• The coordinates of imported boxes don’t align perfectly with the source image.  

• The tool doesn’t support common computer vision annotation formats or requires them to be modified.  

• You cannot export past a certain number of images in one go.

• When a class or attribute is edited, these no longer show up in exports or their changes don’t propagate throughout a dataset.  

• When datasets become really large, database failures cause images to lose their annotations randomly.  

• Annotation histories are not preserved, therefore old versions cannot be restored in case of bad bulk changes.  

• The API doesn’t have a set of easy CLI commands.

• You cannot import annotations into videos.

• The bounding box coordinates round to the wrong pixel.

These are all issues we’ve heard of at least once from customers who were  switching from their internal tools or other labeling platforms.