Commit a805e836 by Tristan Kreuziger

Update README

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# BigEarthNet Deep Models
This repository contains code to use the BigEarthNet archive for deep learning applications.
# Labels
There are two sets of labels available:
* **Full** labels: these labels have a high diversity and are more precise, which can be useful for some applications.
* **Compact** labels: here, some classes have been eliminated or combined to provide a coarser but easier learning experience.
Please find an elaborate discussion about the characteristics of the labels in the literature at the end of this file.
# Usage
_Note_: For now, only code and models for [TensorFlow](https://www.tensorflow.org) are available. Code and pre-trained models for [PyTorch](https://pytorch.org/) are coming soon!
## Generating ML Datasets
After downloading the raw images from https://www.bigearth.net, they need to be prepared for your ML application. We provide the script `prep_splits.py` for this purpose. It generates consumable data files for your ML framework of choice. The following command line arguments can be specified:
* `-r` or `--root_folder`: The root folder containign the raw images you have previously downloaded.
* `-o` or `--out_folder`: The output folder where the resulting files will be created.
* `--update_json`: An optional flag indicating whether or not compact labels shoudl be added to the json file for each patch.
* `-n` or `--patch_names`: A list of CSV files containing the patch names.
Run `python prep_splits.py -h` to see all available parameters.
## Fine-tuning a pre-trained model
The following models have been pre-trained on BigEarthNet and can be fine-tuned:
| Model Name | Pre-Trained TensorFlow Model |
| ------------- |------------------------------:|
| K-Branch CNN | [LINK]() |
| VGG16 | [LINK]() |
| VGG19 | [LINK]() |
| ResNet50 | [LINK]() |
| ResNet101 | [LINK]() |
| ResNet152 | [LINK]() |
An overview of the performance of these models on the BigEarthNet archive can be found in the attached literature.
## Training your own models from scratch
Please check [`train.py`](https://gitlab.tu-berlin.de/rsim/bigearthnet-models-tf/blob/master/train.py) to see how the models can be trained from scratch with BigEarthNet.
# License
The BigEarthNet Archive is licensed under the **Community Data License Agreement – Permissive, Version 1.0** ([Text](https://cdla.io/permissive-1-0/)).
The code in this repository to facilitate the use of the archive is licensed under the **MIT License**:
```
MIT License
Copyright (c) 2019 The BigEarthNet Authors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
# Citations
For the original release and introduction of BigEarthNet, please refer to:
> G. Sumbul, M. Charfuelan, B. Demir, V. Markl, BigEarthNet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding, IEEE International Conference on Geoscience and Remote Sensing Symposium, pp. 5901-5904, Yokohama, Japan, 2019.
```
@article{BigEarthNet,
Author = {Gencer Sumbul and Marcela Charfuelan and Begüm Demir and Volker Markl},
Title = {BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding},
Year = {2019},
Eprint = {arXiv:1902.06148},
Doi = {10.1109/IGARSS.2019.8900532},
Pages = {5901-5904}
}
```
Recently, there has been a follow-up work which introduced compact labels and provided experimental results for several state-of-the-art models. This paper should be cited as:
>...
```
bibtex
```
\ No newline at end of file
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