DeepLabV3+

Model description

DeepLabV3+ is a state-of-the-art semantic segmentation network. It combines the strengths of DeepLabV3 and a powerful encoder-decoder architecture. The network employs atrous convolution to capture multi-scale contextual information effectively. It introduces a novel feature called the "ASPP" module, which utilizes parallel atrous convolutions to capture fine-grained details and global context simultaneously.

Step 1: Installation

pip3 install wandb
pip3 install urllib3==1.26.6

Step 2:Preparing datasets

Sign up and login in Cityscapes official website, then choose 'Download' to download the cityscapes dataset. Specify /path/to/cityscapes to your Cityscapes path in later training process.

The Cityscapes dataset path structure should look like:

Cityscapes
├── leftImg8bit
│   ├── train
│   │   └── aachen
│   │       ├── aachen_000000_000019_leftImg8bit.png
│   │       └── ...
│   └── val
│
├── gtFine
│   ├── train
│   │   └── aachen
│   │       ├── aachen_000000_000019_gtFine_labelTrainIds.png
│   │       └── ...
│   └── val
│
├── license.txt
├── README
├── test.txt
├── train.txt
└── val.txt

Step 3: Training

Open config folder and set /path/to/cityscapes in ./config/cityscapes_resnet50.py. single gpu:

export CUDA_VISIBLE_DEVICES=0
nohup python3 trainer.py cityscapes_resnet50 1> train_deeplabv3.log 2> train_deeplabv3_error.log & tail -f train_deeplabv3.log

Results

Reference

https://github.com/lattice-ai/DeepLabV3-Plus