Pytorch implementation of FCN8 and PSPNet
Download trained Deeplab Resnet model from: https://drive.google.com/open?id=0BxhUwxvLPO7TeXFNQ3YzcGI4Rjg
FCN8 and PSPNet were trained on the PASCAL VOC 2012 Dataset and the BRATS 2017 Dataset.
Results:
FCN8:
Validation: Pixel accuracy: 83%, Mean IU: 43%
 |
 |
 |
PSPNet:
Validation: Pixel accuracy: 82%, Mean IU: 62%
 |
 |
 |
Setup: Install the environment using:
conda env create -f environment.yml
Activate the environment:
source activate psp_env
Start visdom on port 8097:
visdom
Training:
python main.py [-h] --dataset DATASET --model MODEL [--train] [--test]
[--checkpoint CHECKPOINT] --config CONFIG
[--exp_suffix EXP_SUFFIX] [--device {cpu,cuda:0,cuda:1}]
[--image IMAGE] [--label LABEL]
Example:
python main.py --dataset VOC12 --model PSPNet --train --config ../configs/config.ini --exp_suffix 1 --device cpu
Validation:
python main.py --dataset VOC12 --model PSPNet --test --checkpoint <checkpoint_file> --config ../configs/config.ini --exp_suffix 1 --image input_image --label output_image
References: