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:

Clone the repo:

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

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:

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