pytorch-SCNN

**Update on 2018/12/15.

TODO

• Support different backbones
• Support Cityscapes
• Single-GPU training

Introduction

This is a PyTorch(0.4.1) implementation of Spatial CNN . It can use Modified Aligned ResNet as backbone. Currently, we train SCNN using Cityscapes datasets.

Installation

The code was tested with Anaconda and Python 3.6. After installing the Anaconda environment:

0. Clone the repo:

   git clone https://github.com/forlovess/SCNN-pytorch.git
   cd SCNN-pytorch

1. Install dependencies:

   For PyTorch dependency, see pytorch.org for more details.

   For custom dependencies:

   pip install matplotlib pillow tensorboardX tqdm

Training

Fellow steps below to train your model:

1. Input arguments: (see full input arguments via python train.py --help):

   usage: train.py [-h] [--backbone {resnet,drn,mobilenet}] [--path PATH]
               [--out-stride OUT_STRIDE] [--dataset DATASET] [--workers N]
               [--base-size BASE_SIZE] [--crop-size CROP_SIZE]
               [--loss-type {ce,focal}] [--epochs N] [--start_epoch N]
               [--batch-size N] [--test-batch-size N]
               [--use-balanced-weights] [--lr LR]
               [--lr-scheduler {poly,step,cos}] [--momentum M]
               [--weight-decay M] [--nesterov] [--no-cuda]
               [--gpu-ids GPU_IDS] [--seed S] [--resume RESUME]
               [--checkname CHECKNAME] [--eval-interval EVAL_INTERVAL]
               [--no-val]
   

Acknowledgement

This repo is built upon SCNN and LaneNet and pytorch-deeplab-xception.