A novel Depthwise Asymmetric Bottleneck (DAB) module, which efficiently adopts depth-wise asymmetric convolution and dilated convolution to build a bottleneck structure. Based on the DAB module, design a Depth-wise Asymmetric Bottleneck Network (DABNet) especially for real-time semantic segmentation. It creates sufficient receptive field and densely utilizes the contextual information.
| GPU | IXUCA SDK | Release |
|---|---|---|
| BI-V100 | 2.2.0 | 22.09 |
Go to visit COCO official website, then select the COCO dataset you want to download.
Take coco2017 dataset as an example, specify /path/to/coco2017 to your COCO path in later training process, the
unzipped dataset path structure sholud look like:
coco2017
├── annotations
│ ├── instances_train2017.json
│ ├── instances_val2017.json
│ └── ...
├── train2017
│ ├── 000000000009.jpg
│ ├── 000000000025.jpg
│ └── ...
├── val2017
│ ├── 000000000139.jpg
│ ├── 000000000285.jpg
│ └── ...
├── train2017.txt
├── val2017.txt
└── ...pip3 install 'scipy' 'matplotlib' 'pycocotools' 'opencv-python' 'easydict' 'tqdm'bash train_dabnet_dist.sh --data-path /path/to/coco2017/ --dataset coco