It's a simple but complete baseline of the competition.
First you need to download the original data provided and extract the files as follows:
And you need to satisfy the requirements to run the code (The version does not need to be matched):
scipy==1.1.0
numpy==1.17.0
pytorch==1.1.0
torchvision==0.2.2
opencv==3.4.1
pillow==5.1.0
Note: In order to reduce the read time, the read data will be stored in memory. If your PC (or server) doesn't have enough memory, just reduce the dataset.
You need to modify the code by yourself, such as:
- If you don't have a GPU, you need to remove
.cuda()
and.cpu()
in the code. - If there is
out of memory
soon after starting training on GPU, please reducebatch_size
andpatch_size
. (The baseline model is very simple, generally it won't happen.) - ...
This baseline uses a 3-layer 3D convolutional network. Training the model directly will not yield satisfactory results, so you need to modify the model such as increasing the number of convolutional layers.
Process the lost data and convert it into images:
python process.py
This code provides one way to process the dataset. For other data processing and loading methods, please check the forum.
Training:
python train.py
Predicting:
python predict.py
The output files will be in ./result/final/
.
After that, just package the results and upload the archive (Do not pack the final
folder).