[paper
]
To access the main codebase, please switch to the tinypedseg_code branch.
To run the code in this repository, you need to create a Conda environment with the following dependencies:
conda create -n tos_env python=3.9 -y
conda activate tos_env
pip install torch==2.0.0 torchvision==0.15.1 mmsegmentation==0.27.0 mmengine==0.10.1
pip install mmcv-full==1.6.2 -f https://download.openmmlab.com/mmcv/dist/cu117/torch2.0.0/index.html
Ensure you have CUDA 11.7 installed and configured correctly. The required CUDA dependencies should be included with the above packages, but verify with:
nvcc --version
To train a model, use the following command structure:
python tools/train.py --config <path_to_config> --work-dir <path_to_output_directory>
For training with the deeplabv3plus_resnet_tos
configuration:
python tools/train.py --config ./configs/tos/deeplabv3plus_resnet_tos.py --work-dir ./work_dirs/deeplabv3plus_resnet
This repository includes multiple model configurations:
configs/tos/deeplabv3_unet_tos.py
configs/tos/deeplabv3plus_resnet_tos.py
configs/tos/swin_l_tos.py
configs/tos/unet_tos.py
The user should select one of the available configuration files and specify the working directory accordingly.
- Ensure that you have a compatible GPU with CUDA 11.7 support.
- If additional dependencies are required, install them using
pip install <package_name>
. - For further details, refer to the official documentation of the libraries used:
If you find our project helpful, please consider citing our work.
@inproceedings{sahin2023tinypedseg,
title={TinyPedSeg: A Tiny Pedestrian Segmentation Benchmark for Top-Down Drone Images},
author={Sahin, Yusuf H and Abdinli, Elvin and Ayd{\i}n, M Arda and Unal, Gozde},
booktitle={2023 18th International Conference on Machine Vision and Applications (MVA)},
pages={1--5},
year={2023},
organization={IEEE}
}
This implementation builds upon MMSegmentation. We gratefully acknowledge their valuable contributions.