{"id":18792448,"url":"https://github.com/prbonn/dg-cws","last_synced_at":"2025-08-05T10:17:31.285Z","repository":{"id":100745543,"uuid":"587269208","full_name":"PRBonn/DG-CWS","owner":"PRBonn","description":"Towards Domain Generalization in Crop and Weed Segmentation for Precision Farming 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DG-CWS\n*[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)*\n\nWe present a novel approach to leverage unlabeled images captured from various\nagricultural fields to develop domain generalized CNNs that enables agricultural\nrobots to perform a reliable semantic segmentation of the classes soil, crop,\nand weed in different fields.\n\n![Motivation](./static/motivation.png)\n\n# Setup\n```bash\nconda create -n dgcws python=3.8\nconda activate dgcws\npip install -r ./requirements.txt\npip 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\npip install setuptools==59.5.0\n```\n\nPlease note that the cuda version depends on your local machine.\n\n# Training\n\n```python\npython train.py --config ./configs/erfnet/config_uav_bonn.yaml --export_dir \u003c/path/to/export/directory\u003e\n```\n\nBefore you start the training you need to specify the paths to the datasets in the corresponding configuration file.\n\n# Testing\n```python\npython test.py --config ./configs/erfnet/config_uav_bonn.yaml --export_dir \u003c/path/to/export/directory\u003e --ckpt_path \u003cpath/to/erfnet.ckpt\u003e\n```\n\nBefore you start the testing you need to specify the paths to the datasets in the corresponding configuration file.\n\n# Datasets\n- [UAV Bonn](https://uni-bonn.sciebo.de/s/4Nty4gOzTZMy7jj)\n- [UAV Zurich](https://uni-bonn.sciebo.de/s/C1oaCrzntP8ZwPz)\n- [UGV Stuttgart]() -\u003e tba\n- [Sparse Crops](https://uni-bonn.sciebo.de/s/SvXTIrrlneHdwKN)\n- [Sparse Weeds](https://uni-bonn.sciebo.de/s/AJpsgMeWP07W4yY)\n\n# Pretrained Models\n- [ERFNet](http://ipb.uni-bonn.de/html/deeplearningmodels/weyler2023ral/erfnet/erfnet.ckpt)\n- [DeepLabV3+](http://ipb.uni-bonn.de/html/deeplearningmodels/weyler2023ral/deeplabv3plus/deeplab.ckpt)\n\n# License\nThis 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/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprbonn%2Fdg-cws","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprbonn%2Fdg-cws","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprbonn%2Fdg-cws/lists"}