P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic Segmentation [Arxiv]

The paper has been accepted at Expert Systems with Applications ESWA

Code will be released soon, stay tuned!

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

Datasets Preparation

1. Cavmvid Dataset

You can download the Kaggle

Citysscapes Dataset

• You need to download the Cityscapes datasets, unzip them and put the files in the data folder with following structure.

$SEG_ROOT/data\ 
├── Camvid
│       ├── images
│       ├── labels

│    ├── cityscapes
│        ├── gtFine
│            ├── test
│            ├── train
│            ├── val
│     ── ── leftImg8bit
│             ├── test
│             ├── train
│             └── val
│    ├── list
         ├── Camvid
│          ├── test.lst
│          ├── train.lst
│          ├── trainval.lst
│          └── val.lst
│       ├── cityscapes
│          ├── test.lst
│          ├── train.lst
│          ├── trainval.lst
│          └── val.lst
   

Training

Training on Camvid datsaset

• For instance, train the P2AT-S on Camvid dataset with batch size of 8 on 2 GPUs:

python train.py --cfg configs/camvid/p2at_small_camvid.yaml GPUS (0,1) TRAIN.BATCH_SIZE_PER_GPU 4