| | ---
|
| | thumbnail: "https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png"
|
| | tags:
|
| | - efficientnet_b4
|
| | - BigEarthNet v2.0
|
| | - Remote Sensing
|
| | - Classification
|
| | - image-classification
|
| | - Multispectral
|
| | library_name: configilm
|
| | license: mit
|
| | widget:
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| | - src: example.png
|
| | example_title: Example
|
| | output:
|
| | - label: Agro-forestry areas
|
| | score: 0.000000
|
| | - label: Arable land
|
| | score: 0.000000
|
| | - label: Beaches, dunes, sands
|
| | score: 0.000000
|
| | - label: Broad-leaved forest
|
| | score: 0.000000
|
| | - label: Coastal wetlands
|
| | score: 0.000000
|
| | ---
|
| |
|
| | [TU Berlin](https://www.tu.berlin/) | [RSiM](https://rsim.berlin/) | [DIMA](https://www.dima.tu-berlin.de/menue/database_systems_and_information_management_group/) | [BigEarth](http://www.bigearth.eu/) | [BIFOLD](https://bifold.berlin/)
|
| | :---:|:---:|:---:|:---:|:---:
|
| | <a href="https://www.tu.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/tu-berlin-logo-long-red.svg" style="font-size: 1rem; height: 2em; width: auto" alt="TU Berlin Logo"/> | <a href="https://rsim.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png" style="font-size: 1rem; height: 2em; width: auto" alt="RSiM Logo"> | <a href="https://www.dima.tu-berlin.de/menue/database_systems_and_information_management_group/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/DIMA.png" style="font-size: 1rem; height: 2em; width: auto" alt="DIMA Logo"> | <a href="http://www.bigearth.eu/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/BigEarth.png" style="font-size: 1rem; height: 2em; width: auto" alt="BigEarth Logo"> | <a href="https://bifold.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/BIFOLD_Logo_farbig.png" style="font-size: 1rem; height: 2em; width: auto; margin-right: 1em" alt="BIFOLD Logo">
|
| |
|
| | # Efficientnet_b4 pretrained on BigEarthNet v2.0 using Sentinel-2 bands
|
| |
|
| | <!-- Optional images -->
|
| | <!--
|
| | [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) | [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2)
|
| | :---:|:---:
|
| | <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-1"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/sentinel_2.jpg" style="font-size: 1rem; height: 10em; width: auto; margin-right: 1em" alt="Sentinel-2 Satellite"/> | <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-2"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/sentinel_1.jpg" style="font-size: 1rem; height: 10em; width: auto; margin-right: 1em" alt="Sentinel-1 Satellite"/>
|
| | -->
|
| |
|
| | This model was trained on the BigEarthNet v2.0 (also known as reBEN) dataset using the Sentinel-2 bands.
|
| | It was trained using the following parameters:
|
| | - Number of epochs: up to 100 (with early stopping after 5 epochs of no improvement based on validation average
|
| | precision macro)
|
| | - Batch size: 512
|
| | - Learning rate: 0.001
|
| | - Dropout rate: 0.15
|
| | - Drop Path rate: 0.15
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| | - Learning rate scheduler: LinearWarmupCosineAnnealing for 2000 warmup steps
|
| | - Optimizer: AdamW
|
| | - Seed: 42
|
| |
|
| | The weights published in this model card were obtained after 32 training epochs.
|
| | For more information, please visit the [official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts), where you can find the training scripts.
|
| |
|
| | ](https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/combined_2000_600_2020_0_wide.jpg)
|
| |
|
| | The model was evaluated on the test set of the BigEarthNet v2.0 dataset with the following results:
|
| |
|
| | | Metric | Macro | Micro |
|
| | |:------------------|------------------:|------------------:|
|
| | | Average Precision | 0.654173 | 0.749014 |
|
| | | F1 Score | 0.599242 | 0.659746 |
|
| | | Precision | 0.681693 | 0.733997 |
|
| |
|
| | # Example
|
| | | A Sentinel-2 image (true color representation) |
|
| | |:---------------------------------------------------:|
|
| | | ](example.png) |
|
| |
|
| | | Class labels | Predicted scores |
|
| | |:--------------------------------------------------------------------------|--------------------------------------------------------------------------:|
|
| | | <p> Agro-forestry areas <br> Arable land <br> Beaches, dunes, sands <br> ... <br> Urban fabric </p> | <p> 0.000000 <br> 0.000000 <br> 0.000000 <br> ... <br> 0.000000 </p> |
|
| |
|
| |
|
| | To use the model, download the codes that define the model architecture from the
|
| | [official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts) and load the model using the
|
| | code below. Note that you have to install [`configilm`](https://pypi.org/project/configilm/) to use the provided code.
|
| |
|
| | ```python
|
| | from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier
|
| |
|
| | model = BigEarthNetv2_0_ImageClassifier.from_pretrained("path_to/huggingface_model_folder")
|
| | ```
|
| |
|
| | e.g.
|
| |
|
| | ```python
|
| | from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier
|
| |
|
| | model = BigEarthNetv2_0_ImageClassifier.from_pretrained(
|
| | "BIFOLD-BigEarthNetv2-0/efficientnet_b4-s2-v0.1.1")
|
| | ```
|
| |
|
| | If you use this model in your research or the provided code, please cite the following papers:
|
| | ```bibtex
|
| | @article{clasen2024refinedbigearthnet,
|
| | title={reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis},
|
| | author={Clasen, Kai Norman and Hackel, Leonard and Burgert, Tom and Sumbul, Gencer and Demir, Beg{\"u}m and Markl, Volker},
|
| | year={2024},
|
| | eprint={2407.03653},
|
| | archivePrefix={arXiv},
|
| | primaryClass={cs.CV},
|
| | url={https://arxiv.org/abs/2407.03653},
|
| | }
|
| | ```
|
| | ```bibtex
|
| | @article{hackel2024configilm,
|
| | title={ConfigILM: A general purpose configurable library for combining image and language models for visual question answering},
|
| | author={Hackel, Leonard and Clasen, Kai Norman and Demir, Beg{\"u}m},
|
| | journal={SoftwareX},
|
| | volume={26},
|
| | pages={101731},
|
| | year={2024},
|
| | publisher={Elsevier}
|
| | }
|
| | ```
|
| |
|
