- Ubuntu 16.04 LTS
- 64 GB RAM / 2 TB HDD
- 1x NVIDIA Titan V100 32GB
- 1x Titan V 12GB
- Python 3.7.4
- CUDA 10.0
- cuDNN 7.6
- PyTorch 1.1
PLEASE NOTE: Some of the model checkpoints are unfortunately corrupted. Thus certain commands in the inference scripts will not work as intended. Training scripts are available to retrain all the models.
Download from Kaggle:
kaggle datasets download vaillant/siim-ptx-checkpoints
Models should be unzipped into ./segment/checkpoints/ in order to run code as is. There should be 3 folders:
./segment/checkpoints/TRAIN_V100/
./segment/checkpoints/TRAIN_SEGMENT/
./segment/checkpoints/TRAIN_DEEPLABXY/
See entry_points.md for reproducing results. Relative filepaths and directories are used, so the code should work as is.
Note that TRAIN_V100 and TRAIN_DEEPLABXY models require V100 32GB GPUs to train with the current configurations. If you wish to train these models on a lower capacity GPU, I suggest using the following flag options:
--grad-accum 8 --batch-size 2 or --grad-accum 16 --batch-size 1
Model performance is not guaranteed to be the same with these modifications.