Semantic segmentation dataset organization
Question:
I am trying to segment 4 lesions with semantic segmentation. I follow this
this great post
My training folder has only 2 subfolders with patches: masks and images. Inside the folder with masks, ALL the classes are mixed. The other folder has the corresponding images. So, when I train the model ,it appears: ONE CLASS FOUND, just following the abovementioned post. The results are disappointing and I am wondering if I have to split the classes in the folders, and thus the model recognizes 4 classes instead of the one.
Answers:
What your really need to be attentive at is the way in which the masks are created.
It is possible that by default the ImageDataGenerator in Keras to output the number of folders, regardless of how you manually build and adapt the ImageDataGenerator for image segmentation instead of image classification.
My suggestion is to follow the entire post along and change nothing in the first instance. If you pay attention the final results obtained are quite good; this means that the dataset preparation process (mask creation) is correct.
I am trying to segment 4 lesions with semantic segmentation. I follow this
this great post
My training folder has only 2 subfolders with patches: masks and images. Inside the folder with masks, ALL the classes are mixed. The other folder has the corresponding images. So, when I train the model ,it appears: ONE CLASS FOUND, just following the abovementioned post. The results are disappointing and I am wondering if I have to split the classes in the folders, and thus the model recognizes 4 classes instead of the one.
What your really need to be attentive at is the way in which the masks are created.
It is possible that by default the ImageDataGenerator in Keras to output the number of folders, regardless of how you manually build and adapt the ImageDataGenerator for image segmentation instead of image classification.
My suggestion is to follow the entire post along and change nothing in the first instance. If you pay attention the final results obtained are quite good; this means that the dataset preparation process (mask creation) is correct.