This is the implementation for PVT UNet for segmentation

Our implementation is on Python 3.9 , please make sure to config your environment compatible with the requirements.

To install all packages, use requirements.txt file to install. Install with pip by the following command:

pip install -r requirements.txt

All packages will be automatically installed.

For training, use train.py file for start training.

The following command should be used:

python train.py

For inference, use pred.py file to start testing.

The following command should be used:

python pred.py

Note: you should fix model_path for your model path and directory to your benchmark dataset.

The weight for the PVTUNet will be in Google Drive

In our experiment, we use the dataset config from PraNet, with training set from 50% of Kvasir-SEG and 50% of ClinicDB dataset.

With our test dataset, we use the following:

In same distribution:

• Kvasir SEG

• ClinicDB

Out of distribution:

• Etis dataset

• ColonDB

• CVC300