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breizhcrops as bzh\n\n# initialize and download FRH04 data\ndataset = bzh.BreizhCrops(\"frh04\")\n\n# get data sample\nx, y, field_id = dataset[0]\n\n# load pretrained model\nmodel = bzh.models.pretrained(\"Transformer\")\n\n# create a batch of batchsize 1\nx = x.unsqueeze(0)\n\n# perform inference\ny_pred = model(x)\n```\ndownloads the FRH04 dataset partition (used for evaluation), loads a pretrained model and performs a prediction on the first sample.\n\n\n![mimimum working example](doc/breizhcrops_zsh_short.gif)\n\n\nFurthermore, for a detailed data analysis you can check the [Hands-on Tutorial on Time Series](https://colab.research.google.com/drive/1i0M_X5-ytFhF0NO-FjhKiqnraclSEIb0?usp=sharing). This is a Jupyter Notebook for timeseries data exploration with BreizhCrops benchmark.\n\n### Train a model\n\nTrain a model via the example script `train.py`\n```bash\npython train.py TransformerEncoder --learning-rate 0.001 --weight-decay 5e-08 --preload-ram\n```\n\nThis script uses the default model parameters from `breizhcrops.models.TransformerModel`.\nWhen training multiple epochs, the `--preload-ram` flag speeds up training significantly\n\n\n\n### Acknowledgements\n\nThe model implementations from this repository are based on the following papers and github repositories.\n\n* TempCNN (reimplementation from [keras source code](https://github.com/charlotte-pel/igarss2019-dl4sits) ) [Pelletier et al., 2019](https://www.mdpi.com/2072-4292/11/5/523)\n* LSTM Recurrent Neural Network adapted from [Rußwurm \u0026 Körner, 2017](http://openaccess.thecvf.com/content_cvpr_2017_workshops/w18/html/Russwurm_Temporal_Vegetation_Modelling_CVPR_2017_paper.html)\n* MS-ResNet implementation from [Fei Wang](https://github.com/geekfeiw/Multi-Scale-1D-ResNet)\n* TransformerEncoder implementation was originally adopted from Yu-Hsiang Huang [GitHub](https://github.com/jadore801120/attention-is-all-you-need-pytorch), but later replaced by own implementation when `torch.nn.transformer` modules became available\n* InceptionTime [Fawaz et al., 2019](https://arxiv.org/abs/1909.04939)\n* StarRNN [Turkoglu et al., 2019](https://arxiv.org/abs/1911.11033)\n* OmniscaleCNN [Tang et al., 2020](https://arxiv.org/abs/2002.10061)\n\nThe raw label data originates from  \n* [Registre parcellaire graphique (RPG)](https://www.data.gouv.fr/fr/datasets/registre-parcellaire-graphique-rpg-contours-des-parcelles-et-ilots-culturaux-et-leur-groupe-de-cultures-majoritaire/) of the French National Geographic Institute (IGN)\n\n### Reference\n\nThis work will be published in the proceedings of [ISPRS Archives 2020](https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/1545/2020/isprs-archives-XLIII-B2-2020-1545-2020.pdf). [Preprint available on ArXiv](https://arxiv.org/abs/1905.11893)\n```\n@article{breizhcrops2020,\n  title={BreizhCrops: A Time Series Dataset for Crop Type Mapping},\n  author={Ru{\\ss}wurm, Marc and Pelletier, Charlotte and Zollner, Maximilian and Lef{\\`e}vre, S{\\'e}bastien and K{\\\"o}rner, Marco},\n  journal={International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS (2020)},\n  year={2020}\n}\n```\n\nISPRS virtual congress video can be found [here](http://isprs.stream-up.tv/media-221-breizhcrops-a-time-series-dataset-for-crop-type-mapping)\n\n### ICML workshop 2019\n\n\u003ca href=https://arxiv.org/abs/1905.11893\u003e\u003cimg height=300px src=doc/paper.png /\u003e\u003c/a\u003e\n\u003ca href=\"doc/poster.pdf\"\u003e\u003cimg height=300px src=doc/poster.png /\u003e\u003c/a\u003e\n\nA previous version (see [workshop website](http://roseyu.com/time-series-workshop/submissions/2019/timeseries-ICML19_paper_34.pdf) or [arxiv version 1](https://arxiv.org/abs/1905.11893v1)) was presented at the\n presented at the [ICML 2019 Time Series workshop, Long Beach, USA](http://roseyu.com/time-series-workshop/)\nICML workshop contributions do not appear in the ICML proceedings.\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/1101","html_url":"https://ost.ecosyste.ms/projects/1101"}