https://github.com/prbonn/dg-cws
Towards Domain Generalization in Crop and Weed Segmentation for Precision Farming Robots
https://github.com/prbonn/dg-cws
Last synced: 5 months ago
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Towards Domain Generalization in Crop and Weed Segmentation for Precision Farming Robots
- Host: GitHub
- URL: https://github.com/prbonn/dg-cws
- Owner: PRBonn
- License: other
- Created: 2023-01-10T11:05:21.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-05-13T13:01:37.000Z (over 1 year ago)
- Last Synced: 2025-07-12T13:27:07.410Z (6 months ago)
- Language: Python
- Size: 3.34 MB
- Stars: 13
- Watchers: 3
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: license
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README
# DG-CWS
*[Towards Domain Generalization in Crop and Weed Segmentation for Precision Farming Robots](https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/weyler2023ral.pdf)*
We present a novel approach to leverage unlabeled images captured from various
agricultural fields to develop domain generalized CNNs that enables agricultural
robots to perform a reliable semantic segmentation of the classes soil, crop,
and weed in different fields.
# Setup
```bash
conda create -n dgcws python=3.8
conda activate dgcws
pip install -r ./requirements.txt
pip install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
pip install setuptools==59.5.0
```
Please note that the cuda version depends on your local machine.
# Training
```python
python train.py --config ./configs/erfnet/config_uav_bonn.yaml --export_dir
```
Before you start the training you need to specify the paths to the datasets in the corresponding configuration file.
# Testing
```python
python test.py --config ./configs/erfnet/config_uav_bonn.yaml --export_dir --ckpt_path
```
Before you start the testing you need to specify the paths to the datasets in the corresponding configuration file.
# Datasets
- [UAV Bonn](https://uni-bonn.sciebo.de/s/4Nty4gOzTZMy7jj)
- [UAV Zurich](https://uni-bonn.sciebo.de/s/C1oaCrzntP8ZwPz)
- [UGV Stuttgart]() -> tba
- [Sparse Crops](https://uni-bonn.sciebo.de/s/SvXTIrrlneHdwKN)
- [Sparse Weeds](https://uni-bonn.sciebo.de/s/AJpsgMeWP07W4yY)
# Pretrained Models
- [ERFNet](http://ipb.uni-bonn.de/html/deeplearningmodels/weyler2023ral/erfnet/erfnet.ckpt)
- [DeepLabV3+](http://ipb.uni-bonn.de/html/deeplearningmodels/weyler2023ral/deeplabv3plus/deeplab.ckpt)
# License
This software is released under a creative commons license which allows for personal and research use only. For a commercial license please contact the authors. You can view a license summary [here](https://creativecommons.org/licenses/by-nc/4.0/).