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DDRNet-pytorch

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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

    • pytorch==1.7.0
    • 3080*2
    • cuda==11.1

    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. so the result may different from official.

    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 76.83 official
    DDRNet23_slim unknown eval Yes No Yes 77.40 official
    DDRNet23 unknown eval Yes No No 78.41 official
    DDRNet23 unknown eval Yes No Yes 78.85 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

    • Multi-scale with scales: 0.5,0.75,1.0,1.25,1.5,1.75. it runs too slow.
    • from ydhongHIT, can change the align_corners=True with better performance, the default option is False

    Reference

    [1] HRNet-Semantic-Segmentation OCR branch

    [2] the official repository