knotes | Note-taking application | Dektop Application library

I first explain my task: I have nearly 3000 images from two different ropes. They contain rope 1, rope 2 and the background. My Labels/Masks are images, where for example the pixel value 0 represents the background, 1 represents the first rope and 2 represents the second rope. You can see both the input picture and the ground truth/labels here on picture 1 and 2 below. Notice that my ground truth/label has only 3 values: 0, 1 and 2. My input picture is gray, but for DeepLab i converted it to a RGB Picture, because DeepLab was trained on RGB Pictures. But my converted picture still doesn't contain color.

The idea of this task is that the Neural Network should learn the structure from ropes, so it can label ropes correctly even if there are knotes. Therfore the color information is not important, because my ropes have different color, so it is easy to use KMeans for creating the ground truth/labels.

For this task i choose a Semantic Segmentation Network called DeepLab V3+ in Keras with TensorFlow as Backend. I want to train the NN with my nearly 3000 images. The size of alle the images is under 100MB and they are 300x200 pixels. Maybe DeepLab is not the best choice for my task, because my pictures doesn't contain color information and the size of my pictures are very small (300x200), but i didn't find any better Semantic Segmentation NN for my task so far.

From the Keras Website i know how to load the Data with flow_from_directory and how to use the fit_generator method. I don't know if my code is logical correct...

Here are the links:

https://keras.io/preprocessing/image/

https://keras.io/models/model/

https://github.com/bonlime/keras-deeplab-v3-plus

My first question is:

With my implementation my graphic card used nearly all the memory (11GB). I don't know why. Is it possible, that the weights from DeepLab are that big? My Batchsize is default 32 and all my nearly 300 images are under 100MB big. I already used the config.gpu_options.allow_growth = True code, see my code below.

A general question:

Does somebody know a good semantic segmentation NN for my task? I don't need NN, which were trained with color images. But i also don't need NN, which were trained with binary ground truth pictures... I tested my raw color image(picture 3) with DeepLab, but the result label i got was not good...

Here is my code so far: