Mask R-CNN annotation tool

Question

I’m new to deep learning and I was reading some state of art papers and I found that mask r-cnn is utterly used in segmentation and classification of images. I would like to apply it to my MSc project but I got some questions that you may be able to answer. I apologize if this isn’t the right place to do it. First, I would like to know what are the best strategy to get the annotations. It seems kind of labor intensive and I’m not understanding if there is any easy way. Following that, I want to know if you know any annotation tool for mask r-cnn that generates the binary masks that are manually done by the user.

I hope this can turn into a productive and informative thread so any suggestion, experience would be highly appreciated.

Regards

Answer 1

You can use MASK-RCNN, I recommend it, is a two-stage framework, first you can scan the image and generate areas likely contain an object. And the second stage classifies the proposal drawing bounding boxes.

But the two-big question

how to train a model from scratch? And What happens when we want to train our own dataset?

You can use annotations downloaded from the internet, or you can start creating your own annotations, this takes a lot of time!

You have tools like:

VIA GGC image annotator http://www.robots.ox.ac.uk/~vgg/software/via/via_demo.html

it's online and you don't have to download any program. It is the one that I recommend you, save the images in a .json file, and so you can use the class of ballons that comes by default in SAMPLES in the framework MASK R-CNN, you would only have to put your json file and your images and to train your dataset.

But there are always more options, you have labellimg which is also used for annotation and is very well known but save the files in xml, you will have to make a few changes to your Class in python. You also have labelme, labelbox, etc.