Few-shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation

Link to our Paper: https://arxiv.org/abs/2308.02790

Prerequisites and Requirements

We tested our code under Ubuntu 20.04 and Windows with

- Python 3.11.4
- Cuda 11.8
- PyTorch 2.0.1

To run our code please proceed as follows:

1. Install all packages from the requierements.txt in the root folder.
2. You must specify the absolute path of the Dataset and Checkpoints directories in the Path.json file of the project root directory
3. Download the Cityscapes dataset and KITTI dataset,then put it in a folder "Dataset".For the Cityscapes dataset, you need to download some JSON files,and extract them before placing them in the folder "cityscapes".
4. Your Dataset folder should look like this:

├───cityscapes
│   ├───gtFine
│   │   ├───test
│   │   ├───train
│   │   └───val
│   └───leftImg8bit
│       ├───test
│       ├───train
│       └───val
└───kitti
    ├───test
    └───train

Evaluation

You can evaluate the trained models with the following command:

bash ./evaluate.sh

Training

The following commands can be used to train the model:

bash ./train.sh

Pre-trained models

You can download our pre-trained models here,and unzip to the project root directory.

License

The ERFNet model used in this project was developed by E. Romera et al. here. The project was released under the Creative Commons Attribution-NonCommercial 4.0 International License. Our Code is also licensed under the Creative Commons Attribution-NonCommercial 4.0 International License, which allows for personal and research use only.

View the license summary here: http://creativecommons.org/licenses/by-nc/4.0/