Super-BPD for Fast Image Segmentation (CVPR 2020)
Introduction
We propose direction-based super-BPD, an alternative to superpixel, for fast generic image segmentation, achieving state-of-the-art real-time result.
Dataset
- Download the BSDS500 & PascalContext Dataset, and unzip it into the
Super-BPD/data
folder.
Testing
- Compile cuda code for post-process.
cd post_process
python setup.py install
-
Download the pre-trained PascalContext model and put it in the
saved
folder. -
Test the model and results will be saved in the
test_pred_flux/PascalContext
folder. -
SEISM is used for evaluation of image segmentation.
Training
- Download VGG-16 pretrained model.
python train.py --dataset PascalContext