Question regarding augmented dataset size described in paper

[convolutionalJellyfish]

Hello,

First of all, thank you very much for your contributions to the robotics community, and for making the code for your paper publicly available.

I have a (hopefully simple) question regarding the augmented dataset size described in your paper.
Specifically, the augmented dataset size described in the following lines:

The extended version of Cornell Grasp Dataset comprises of 1035 RGB-D images with a resolution of 640×480 pixels of 240 different real objects with 5110 positive and 2909 negative grasps. The annotated ground truth consists of several grasp rectangles representing grasping possibilities per object.
However, it is a small dataset for training our GR-ConvNet model, therefore we create an augmented dataset using random crops, zooms, and rotations which effectively has 51k grasp examples. Only positively labeled grasps from the dataset were considered during training.

Would you be able to please let me know how the number 51k was obtained?
From trying to recreate the same calculations, I have only been able to get either 39k or 46k depending on how some quantities are defined.

Thank you very much for your time!

[AbdelrahmanMoh97]

Hello,

Have you been able to know how are the 51K grasps obtained ?

In the train_network.py it sets the random_zoom, crop .. etc to true which means that dataset becomes augmented however, the dataset size I get is still the same which confuses me.

Thanks in advance.