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chenjun2hao / DDRNet.pytorch

This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data
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ddrnet pytorch real-time segmentation semantic-segmentation

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Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes

Introduction

This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data!on single 2080Ti GPU, DDRNet-23-slim yields 77.4% mIoU at 109 FPS on Cityscapes test set and 74.4% mIoU at 230 FPS on CamVid test set.

The code mainly borrows from HRNet-Semantic-Segmentation OCR and the official repository, thanks for their work.

hrnet

requirements

Here I list the software and hardware used in my experiment

Quick start

0. Data preparation

You need to download the Cityscapesdatasets. and rename the folder cityscapes, then put the data under data folder. 

└── data
  ├── cityscapes
  └── list

1. Pretrained model

download the pretrained model on imagenet or the segmentation model from the official，and put the files in ${PROJECT}/pretrained_models folder

VAL

use the official pretrained model and our eval.py code. with ydhongHIT's advice now can reach the same accuracy in the paper. Thanks.

cd ${PROJECT}
python tools/eval.py --cfg experiments/cityscapes/ddrnet23_slim.yaml
model Train Set Test Set OHEM Multi-scale Flip mIoU Link
DDRNet23_slim unknown eval Yes No No 77.83 official
DDRNet23_slim unknown eval Yes No Yes 78.42 official
DDRNet23 unknown eval Yes No No 79.51 official
DDRNet23 unknown eval Yes No Yes 79.98 official

Note

TRAIN

download the imagenet pretrained model, and then train the model with 2 nvidia-3080

cd ${PROJECT}
python -m torch.distributed.launch --nproc_per_node=2 tools/train.py --cfg experiments/cityscapes/ddrnet23_slim.yaml

the own trained model coming soon

OWN model

model Train Set Test Set OHEM Multi-scale Flip mIoU Link
DDRNet23_slim train eval Yes No Yes 77.77 Baidu/password:it2s
DDRNet23_slim train eval Yes Yes Yes 79.57 Baidu/password:it2s
DDRNet23 train eval Yes No Yes ~ None
DDRNet39 train eval Yes No Yes ~ None

Note

Reference

[1] HRNet-Semantic-Segmentation OCR branch

[2] the official repository