- Integrate Higra to generate multiscale hierarchy
- Check training of orientation maps
- Check results with original caffe implementation
PyTorch implementation of Convolutional Oriented Boundaries
- ResNet50 from pyTorch model zoo, which differs from author's Caffe model (has batch normalization layers)
- Batch size of 16
- Base learning-rate is 1e-4 and is increased for "deeper" layers
- Weight initialization is gaussian/normal instead of constant
Most of these are easily installed with your favorite package manager
- PyTorch >= 1.0
- Numpy
- Scipy
- imgaug
- opencv-python
- tqdm
- imgaug
- sklearn
- tensorboardX
- higra
Download and uncompress the following datasets/annotations:
- Pascal VOC 2012 Unzip this in /pascal-voc
- Pascal-Context Unzip this in /trainval
On first run, the whole dataset (>10k images) will be processed to extract boundaries, this can take more than an hour!
python train.py --root-imgs <root>/pascal-voc/VOC2012 --root-segs <root>/trainval --run-path <root>/runs/cob --cuda