DSNet：A Novel Way to Use Atrous Convolutions in Semantic Segmentation

a novel real-time model in semantic segmentation This is the implementation for DSNet.

Environment:

PyTroch 1.10

python 3.8

4*RTX4090 or 8*RTX4090

  pip install -r requirements.txt

Highlight

Params vs mIOU on Cityscapes val set

ADE20K

• We revisited the design of atrous convolutions in CNNs,and explored three empirical guidelines for atrous convolution. Based on the above guidelines, we proposed a novel Dual-branch network.

• DSNet achieves a new state-of-the-art trade-off between accuracy and speed on ADE20K, Cityscapes,and BDD10K.

Overview:

An overview of the basic architecture of our proposed DSNet.

Train and Inference speed:

This implementation is based on HRNet-Semantic-Segmentation. Please refer to their repository for installation and dataset preparation.The inference speed is tested on single RTX 3090 or RTX4090. BDD10K has not been implemented in the above link. The dataset storage format is as follows. Download link: web page

bdd
- seg
  - color_labels
    - train
    - val
  - images
    - train
    - val
    - test
  - labels
    - train
    - val

Train

  python -m torch.distributed.launch --nproc_per_node=4 DSNet/tools/train.py

Inference speed

  python DSNet/models/speed/dsnet_speed.py

Weight

DSNet-Base:

DSNet_Base_imagenet： Baidu drive ,google drive

ADE20K: 43.44%mIOU: Baidu drive, google drive

BDD10K: 64.6%mIOU： Baidu drive, google drive

Camvid(pretrained on Cityscapes train set): 83.32%mIOU: Baidu drive, google drive

Cityscapes : 82.0%mIOU:google drive

DSNet:

DSNet_imagenet: Baidu drive, google drive

ADE20k 40.0%mIOU: Baidu drive, google drive

BDD10K 62.8%mIOU: Baidu drive, google drive

Cityscapes: 80.4%mIOU:google drive

xlflove/DSNet