dutchf3_patch

Dutch F3 Patch Experiments

In this folder are training and testing scripts that work on the F3 Netherlands dataset. You can run five different models on this dataset:

• HRNet
• SEResNet
• UNet
• PatchDeconvNet
• PatchDeconvNet-Skip

All these models take 2D patches of the dataset as input and provide predictions for those patches. The patches need to be stitched together to form a whole inline or crossline.

To understand the configuration files and the default parameters refer to this section in the top level README

Setup

Please set up a conda environment following the instructions in the top-level README.md file. Also follow instructions for downloading and preparing the data.

Running experiments

Now you're all set to run training and testing experiments on the Dutch F3 dataset. Please start from the train.sh and test.sh scripts, which invoke the corresponding python scripts. If you have a multi-GPU machine, you can also train the model in a distributed fashion by running train_distributed.sh. Take a look at the project configurations in (e.g in default.py) for experiment options and modify if necessary.

Please note that we use NVIDIA's NCCL library to enable distributed training. Please follow the installation instructions here to install NCCL on your system.

Monitoring progress with TensorBoard

• from the this directory, run tensorboard --logdir='output' (all runtime logging information is written to the output folder
• open a web-browser and go to either <vm_public_ip>:6006 if running remotely or localhost:6006 if running locally

NOTE:If running remotely remember that the port must be open and accessible

More information on Tensorboard can be found here.