Labeling
Create bounding box labels to teach your AI how to detect objects within an image or video.
Last updated
Create bounding box labels to teach your AI how to detect objects within an image or video.
Last updated
Click the Start labeling button on the top right corner to start teaching your AI what you want it to detect.
Click on the class you want to label or press the shortcut number in your keyboard to select it.
Place your mouse at one of the corners of the object you want to label. Then click and drag your mouse to create a new bounding box, finally release your mouse when you have encapsulated the object in the tightest possible manner.
Labels must encapsulate its object in the most tight and precise way possible, meaning no room has to be left between the bounding box and the contours of the object. If you accidentally made the bounding box bigger or smaller than the object, keep the space key pressed to enter into the transform mode (or click the hand icon in the top left corner) and fix your label. If the object is partially occluded by another object, make your best guess and draw the bounding box up until where you think the whole object contour will likely be.
Perfectly create all the labels you would want your AI to detect in this example image. Make sure no object is kept unlabeled, as this will confuse your AI and it won't perform well in production. This is very important. A single unlabeled object can significantly impact the accuracy of your AI.
After you finished labeling the whole image, press the E shortcut key to submit your labels or click the Submit button in the bottom left corner. If image happens to don't have any objects in it, press the Q shortcut key to skip it, or click the Skip button.
You can inspect your dataset statistics in the bottom of its overview page. You should always strive to have a balanced dataset, meaning that all your classes have roughly the same number of labels. The more labels a class has, the more examples your AI will have to correctly learn from, and if another class has significantly fewer labels, your AI might mislabel it as the class with more labels or don't recognize it at all. But don't worry if the intrinsic distribution of your data does not allow for this. For example, in this use case people will always have twice more eyes than mouthes, noses, and faces.
