Bi-Directional Cascade Network for Perceptual Edge Detection(BDCN)

This paper proposes a Bi-Directional Cascade Network for edge detection. By introducing a bi-directional cascade structure to enforce each layer to focus on a specific scale, BDCN trains each network layer with a layer-specific supervision. To enrich the multi-scale representations learned with a shallow network, we further introduce a Scale Enhancement Module (SEM). Here are the code for this paper.

Prerequisites

Download and install Anaconda

- pytorch >= 0.2.0(Our code is based on the 0.2.0)
- numpy >= 1.11.0
- pillow >= 3.3.0

wget https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh
sh Anaconda3-2019.03-Linux-x86_64.sh
conda init

Use conda activate/deactivate to en- or disable conda environment.

Depending on your setup follow the instructions from Pytorch.

conda install ninja pyyaml mkl mkl-include setuptools cmake cffi typing torchvision scipy numpy pillow opencv matplotlib pytorch=0.4.1 cuda92 python=2.7 -c pytorch

Test Pytorch installation

conda activate bdcn
python

from __future__ import print_function
import torch
x = torch.rand(5, 3)
print(x)

Test CUDA

conda activate bdcn
python

import torch
torch.cuda.is_available()

Train and Evaluation

1. Clone this repository to local

git clone https://github.com/pytorch/pytorch.git

2. Download the imagenet pretrained vgg16 pytorch model [vgg16.pth](link: https://pan.baidu.com/s/10Tgjs7FiAYWjVyVgvEM0mA code: ab4g) or the caffemodel from the model zoo and then transfer to pytorch version. You also can download our pretrained model for only evaluation. The google drive link.

3. Download the dataset to the local folder

4. running the training code train.py or test code test.py

Pretrained models

BDCN model for BSDS500 dataset and NYUDv2 datset of RGB and depth are availavble on Baidu Disk.

The link https://pan.baidu.com/s/18PcPQTASHKD1-fb1JTzIaQ
code: j3de

The pretrained model will be updated soon.