Images for segmentation of optical coherence tomography images with diabetic macular edema.
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You can download dataset from here
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Download and unzip the data on Unet directory
First install all the necessary dependencies
pip3 install -r requirements.txt- Download the dataset and save it in Unet directory
- To train, test and save your own model first import the Unet module
import Unet"""
width_out : width of the output image
height_out : height of the output image
width_in : width of the input image
height_in : height of the input image
"""
unet = Unet.Unet(inchannels, outchannnels)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(unet.parameters(), lr = 0.01, momentum=0.99)
outputs = outputs.permute(0, 2, 3, 1)
m = outputs.shape[0]
outputs = outputs.resize(m*width_out*height_out, 2)
labels = labels.resize(m*width_out*height_out)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()To know more checkout run_unet.py
Go to this to checkout implementation and functioning of Unet Networks.
