sainatarajan

> If anyone still has this issue with reading RGB images or getting errors please look for me. Went through the same and found a fix for ti Can you...

I think: You have a set of images and a set of labels for those images. The labels are just the masks of those images. The area of the image...

I have obtained the solution. All you have to do is to add the BatchNormalization after every Conv2D layer except for the last Conv2D layer. Ex: ``` conv1 = Conv2D(64,...

> Thanks a lot. Still I'm facing same issue. Code s available in the link below > https://github.com/sudhasubramaniam/segmentation You need to add the BatchNormalization to the up-sampling layers as well....

> No change. Still facing same issue. Try the code from my repo. Link [here](https://github.com/sainatarajan/U-Net).

Try to keep only the boundary information on the labels. Blackout all areas except for the boundary region so that the network focuses on the specific region. Ex : ...

Hi, The possible reasons why your model has failed are: 1. The image set has extra information. Can you remove all those? Like Length, LCCA, + Markers and other such...

@BriFuture I will tell you the simple steps to create the masks. Assuming you have 2 classes: 1. Open the JSON file in python and process image by image. 2....

One color for one class. The regions that are not of interest make it black. And also make sure you change the loss function from binary_crossentropy if you are dealing...

> I searched google and find this repo which contains 4 classes. Ok, so you can also color your labels(masks) in this way (1, 1, 1) or (2, 2, 2)...