ResUNet improvement about deep-residual-unet HOT 3 CLOSED

The training loss seems to be much lower than the validation loss or vice versa. i.e. I guess your model is overfitting. Which dataset you used?

from deep-residual-unet.

The data seems to be quite sparse than the networks. I guess this can be solved by using earlyStopping, dropout and augmenting the data.

from deep-residual-unet.

Yes, arghadeep25, I think you write, there are too many epochs (120 epochs). So it seems overfitting.

The dataset is from Kaggle TGS Salt Identification Challenge: https://www.kaggle.com/c/tgs-salt-identification-challenge
Original Jupyter Notebook: https://github.com/nikhilroxtomar/Deep-Residual-Unet/blob/master/Deep%20Residual%20UNet.ipynb
You can open and run it via Google Colab: https://github.com/foobar167/articles/blob/master/Machine_Learning/code_examples/deep_residual_unet_segmentation.ipynb

from deep-residual-unet.