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  ---
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- thumbnail: "https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png"
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  tags:
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- - resnet101
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- - BigEarthNet v2.0
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- - Remote Sensing
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- - Classification
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- - image-classification
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- - Multispectral
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- library_name: configilm
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- license: mit
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- widget:
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- - src: example.png
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- example_title: Example
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- output:
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- - label: Agro-forestry areas
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- score: 0.000000
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- - label: Arable land
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- score: 0.000000
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- - label: Beaches, dunes, sands
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- score: 0.000000
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- - label: Broad-leaved forest
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- score: 1.000000
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- - label: Coastal wetlands
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- score: 0.000000
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  ---
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- [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/)
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- :---:|:---:|:---:|:---:|:---:
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- <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">
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-
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- # Resnet101 pretained on BigEarthNet v2.0 using Sentinel-1 & Sentinel-2 bands
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-
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- <!-- Optional images -->
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- <!--
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- [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) | [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2)
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- :---:|:---:
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- <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"/>
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- -->
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-
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- This model was trained on the BigEarthNet v2.0 (also known as reBEN) dataset using the Sentinel-1 & Sentinel-2 bands.
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- It was trained using the following parameters:
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- - Number of epochs: up to 100
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- - with early stopping
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- - after 5 epochs of no improvement
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- - based on validation average precision (macro)
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- - the weights published in this model card were obtained after 18 training epochs
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- - Batch size: 512
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- - Learning rate: 0.001
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- - Dropout rate: 0.15
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- - Drop Path rate: 0.15
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- - Learning rate scheduler: LinearWarmupCosineAnnealing for 1000 warmup steps
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- - Optimizer: AdamW
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- - Seed: 42
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-
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- The model was trained using the training script of the
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- [official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts).
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- See details in this repository for more information on how to train the model given the parameters above.
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-
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- ![[BigEarthNet](http://bigearth.net/)](https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/combined_2000_600_2020_0_wide.jpg)
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-
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- The model was evaluated on the test set of the BigEarthNet v2.0 dataset with the following results:
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-
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- | Metric | Value Macro | Value Micro |
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- |:------------------|------------------:|------------------:|
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- | Average Precision | 0.714370 | 0.867973 |
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- | F1 Score | 0.638747 | 0.768663 |
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- | Precision | 0.801060 | 0.816409 |
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-
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- # Example
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- | Example Input (only RGB bands from Sentinel-2) |
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- |:---------------------------------------------------:|
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- | ![[BigEarthNet](http://bigearth.net/)](example.png) |
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-
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- | Example Output - Labels | Example Output - Scores |
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- |:--------------------------------------------------------------------------|--------------------------------------------------------------------------:|
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- | <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> |
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-
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-
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- To use the model, download the codes that defines the model architecture from the
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- [official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts) and load the model using the
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- code below. Note, that you have to install `configilm` to use the provided code.
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-
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- ```python
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- from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier
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-
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- model = BigEarthNetv2_0_ImageClassifier.from_pretrained("path_to/huggingface_model_folder")
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- ```
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-
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- e.g.
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-
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- ```python
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- from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier
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-
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- model = BigEarthNetv2_0_ImageClassifier.from_pretrained(
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- "BIFOLD-BigEarthNetv2-0/BENv2-resnet101-all-v0.1.1")
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- ```
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-
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- If you use this model in your research or the provided code, please cite the following papers:
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- ```bibtex
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- CITATION FOR DATASET PAPER
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- ```
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- ```bibtex
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- @article{hackel2024configilm,
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- title={ConfigILM: A general purpose configurable library for combining image and language models for visual question answering},
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- author={Hackel, Leonard and Clasen, Kai Norman and Demir, Beg{\"u}m},
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- journal={SoftwareX},
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- volume={26},
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- pages={101731},
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- year={2024},
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- publisher={Elsevier}
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- }
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- ```
 
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  ---
 
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  tags:
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+ - model_hub_mixin
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+ - pytorch_model_hub_mixin
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
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+ - Library: [More Information Needed]
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+ - Docs: [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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