Can't able to train the num_classes=1

model =SemanticSegmentationTask(
    segmentation_model="fcn",
    encoder_name="resnet18",
    encoder_weights="imagenet",
    in_channels=9,
    num_classes=1,
    num_filters=128,
    loss="jaccard",
    ignore_zeros=True,
    learning_rate=0.1,
    learning_rate_schedule_patience=0.05,
    
)

trainer = pl.Trainer(

                  gpus=0,
                  callbacks=[checkpoint_callback, early_stopping_callback],
                  logger=[csv_logger],
                  min_epochs=10,
                  max_epochs=100,
                  precision=32,
                  log_every_n_steps=0.01,
                  max_steps=8,
                  
               )

trainer.fit(model,dl,dl)

Hi, as I want to do the binary sematic segmentation I need num_clasess=1 can I have any scope of doing with single class

[calebrob6]

Yeah probably, I just don’t have time to play around with it 😃

On Fri, May 6, 2022 at 1:10 PM Adam J. Stewart @.***> wrote:

Could we add a conditional that automatically switches to BCE for num_classes=1?

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[adamjstewart]

I think this is also related to #245. So it’s not just loss, it’s also IoU.