End of Week 2 - 15/06/2020
What did you do this week?
I worked on getting the images stored in a directory to enter the network. For training data, the directory source reads the images and labels them with the same name as the sub-folder they are present in inside the train folder. The directory structure looks like this for training (supervised classification) :-
| -- dataset
| -- | -- train
| -- | -- | -- label_1
| -- | -- | -- | -- image_1.png
| -- | -- | -- | -- image_2.png
| -- | -- | -- | -- .....
| -- | -- | -- label_2
| -- | -- | -- | -- image_1.png
| -- | -- | -- | -- image_2.png
| -- | -- | -- | -- .....
I recently added a resize operation which would resize every image to be resized to the given provided new dimensions using the OpenCV resize function which will be reviewed by my mentor soon. I added a new flag to the CLI commands to display record data in a tabular format instead of JSON dumping them so as to make it easy to with long image arrays in a better way!
What is coming up next?
What is coming up next?
I will be working on finalising the directory source to accommodate for unsupervised classification too and on adding more functions from the OpenCV library to manipulate the image arrays and extract their features.
Did you get stuck anywhere?
Found out that the existing scikit models in DFFML are not able to train on features that are not single valued. So, I am trying to find a way to fix this bug for now.
Thank you for reading!