Artifacts Detection (Draft)

Wrinkle Detection

For pre-processing, we create a folder datasets/Wrinkle/train/, and put (or soft link) the training images and wrinkle labels under the folder, which perserves a directory structure like images/**/*.png and wrinkles/**/*.png. The model will randomly sample patches from the large images at training time and run sliding-window inference at test time, with the image resolution unchanged.

The config file for training wrinkle detection model from EM images is configs/misc/Wrinkle-Deeplab-Binary-2D.yaml. We have tested the training with 2 Nvidia V100 GPUs and 16 CPU cores. After intalling the package, run

source activate py3_torch
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -u -m torch.distributed.run \
--nproc_per_node=2 --master_port=1234 scripts/main.py --distributed \
--config-file configs/misc/Wrinkle-Deeplab-Binary-2D.yaml

The model checkpoints will be saved to outputs/Wrinkle_Deeplab/. For inference on test images, run

source activate py3_torch
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -u scripts/main.py --inference \
--config-file configs/misc/Wrinkle-Deeplab-Binary-2D.yaml \
--checkpoint outputs/Wrinkle_Deeplab_new/checkpoint_100000.pth.tar