DSRL

Implementation of CVPR 2020 Dual Super-Resolution Learning for Semantic Segmentation

pytorch-deeplab-xception

Update on 2018/12/06. Provide model trained on VOC and SBD datasets.

Update on 2018/11/24. Release newest version code, which fix some previous issues and also add support for new backbones and multi-gpu training. For previous code, please see in previous branch

TODO

[x] Support different backbones
[x] Support VOC, SBD, Cityscapes and COCO datasets
[x] Multi-GPU training

Backbone	train/eval os	mIoU in val	Pretrained Model
ResNet	16/16	78.43%	google drive
MobileNet	16/16	70.81%	google drive
DRN	16/16	78.87%	google drive

Introduction

This is a PyTorch(0.4.1) implementation of DeepLab-V3-Plus. It
can use Modified Aligned Xception and ResNet as backbone. Currently, we train DeepLab V3 Plus
using Pascal VOC 2012, SBD and Cityscapes datasets.

results-1

Installation

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

Clone the repo:

git clone https://github.com/jfzhang95/pytorch-deeplab-xception.git
cd pytorch-deeplab-xception

Install dependencies:

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

For custom dependencies:
```
pip install matplotlib pillow tensorboardX tqdm
```

Training

Follow steps below to train your model:

Configure your dataset path in mypath.py.

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

usage: train.py [-h] [--backbone {resnet,xception,drn,mobilenet}]
            [--out-stride OUT_STRIDE] [--dataset {pascal,coco,cityscapes}]
            [--use-sbd] [--workers N] [--base-size BASE_SIZE]
            [--crop-size CROP_SIZE] [--sync-bn SYNC_BN]
            [--freeze-bn FREEZE_BN] [--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] [--ft] [--eval-interval EVAL_INTERVAL]
            [--no-val]

To train deeplabv3+ using Pascal VOC dataset and ResNet as backbone:
```
bash train_voc.sh
```
To train deeplabv3+ using COCO dataset and ResNet as backbone:
```
bash train_coco.sh
```