Keras-Semantic-Segmentation | Keras-Semantic-Segmentation | Machine Learning library

I use Segmentation Models library for multi-class (in my case 4 class) semantic segmentation. The model (UNet with 'resnet34' backbone) is trained with 3000 RGB (224x224x3) images. The accuracy is around 92.80%.

1) Why model.predict() function requires (1,224,224,3) shaped array as input ? I didn't find the answer even in the Keras documentation. Actually, below code is working, I have no problem with it but I want to understand the reason.

I am using a convolutional neural net (CNN) called make_unet from here. It works and code is able to run with this CNN. But I know that in deep learning you have to initialize weights for optimization of the neural network.

The documentation in Keras clearly indicates the use of a kernel_initializer for weight initialization. However, I do not see any kernel_initializer in the make_unet function I am using.

Anyone who can provide some insight would be appreciated.

I try to augment my image data using the Keras ImageDataGenerator. My task is a regression task, where an input image results in another, transformed image. So far so good, works quite well.

Here I wanted to apply data augmentation by using the ImageDataGenerator. In order to transform both images the same way, I used the approach described in the Keras docs, where a transformation of an image with a corresponding mask is described. My case is a little bit different, as my images are already loaded and don't need to be fetched from a directory. This procedure was already described in another StackOverlow post.

To verify my implementation, I first used it without augmentation and using the ImageDataGenerator without any parameter specified. According to the class reference in the Keras docs, this should not alter the images. See this snippet:

I would like to follow the Convolutional Neural Net (CNN) approach here. However, this code in github uses Pytorch, whereas I am using Keras.

I want to reproduce boxes 6,7 and 8 where pre-trained weights from VGG-16 on ImageNet is downloaded and is used to make the CNN converge faster.

In particular, there is a portion (box 8) where weights are downloaded and skipped from VGG-16 that have no counterpart in SegNet (the CNN model). In my work, I am using a CNN model called U-Net instead of Segnet. The U-Net Keras code that I am using can be found here.

I am new to Keras and would appreciate any insight in Keras code on how I can go about downloading and skipping the VGG weights that have no counterpart with my U-Netmodel.

I am new to Python and I am trying to use the function 'one_hot_to_label_batch' which can be found on line 115 from this website.

However, directly above this function there is a '@expand_dims'. This is the first time I have encountered this. I know 'expand_dims' is within Numpy but I do not know why it is defined as '@expand_dims' here. Any clarification would be appreciated.

I am using code from this website

There is a function called 'label_to_one_hot_batch' within the file `isprs.py.

In my own python script in the main directory folder (i.e., the semseg folder), I am trying to import this function as follows: