# Are there any easy ways to create annotated training images for object detection?

For the purposes of object detection, are there any easy ways to create annotated training images? For example, if we have $$10,000$$ images and want to draw bounding boxes on 2 objects for each image, do we have to physically draw those boxes? Is that what most people do these days to create training data?

• I would suggest you to get a look at what openCV can do. I don't think you'll need to draw boxes but you may have to answer manually for each picture if it's object A or B. Nov 23 '19 at 21:00
• Possible duplicate of Are there tools to help labelling images?, but maybe this is not an exact duplicate.
– nbro
Nov 24 '19 at 0:45
• None of these comments are in the direction of the question. James is asking whether there is a smarter way to automatically annotate large amount of training images. Here is my input. The short answer is: it depends. If objects are certain know objects that you may be able to use a trained model, you may have some decent annotations, but I am afraid that is likely. Google now does it with AutoML. Besides, you do not need more than 300-400 annotations per each object. Why you think you need to train 10,000 images in the first place? Nov 24 '19 at 13:53
• I once had a fresh dataset and needed to draw bboxes. I made my own little python+opencv program. I always assumed that people in similar circumstances would do the same...but perhaps a wee bit different depending on the needs of the problem. So, the answer is yes, the boxes need to be draw by somebody and if it's your task, then you're the best person to do it. When I did it, I learned a lot about my images as well. Nov 25 '19 at 21:55
• If you don't manually enter in the information, you expect a system to. Which becomes circular since you're trying to teach a system to detect images.... Mar 8 '20 at 23:36