Upload mixer_b16_224-s1-v0.1.1
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README.md
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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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- 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: 0.000052
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- label: Arable land
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score: 0.000312
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- label: Beaches, dunes, sands
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score: 0.001637
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- label: Broad-leaved forest
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score: 0.000303
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- label: Coastal wetlands
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score: 0.000325
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---
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# Mixer_b16_224 pretained on BigEarthNet v2.0 using Sentinel-1 bands
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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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This model was trained on the BigEarthNet v2.0 (also known as reBEN) dataset using the Sentinel-1 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 17 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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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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![[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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The model was evaluated on the test set of the BigEarthNet v2.0 dataset with the following results:
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| Metric | Value Macro | Value Micro |
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|:------------------|------------------:|------------------:|
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| Average Precision | 0.552686 | 0.737118 |
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| F1 Score | 0.503817 | 0.643113 |
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| Precision | 0.585352 | 0.712074 |
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# Example
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| Example Input (VV, VH and VV/VH bands from Sentinel-1) |
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|:---------------------------------------------------:|
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| ![[BigEarthNet](http://bigearth.net/)](example.png) |
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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> 0.000052 <br> 0.000312 <br> 0.001637 <br> ... <br> 0.000070 </p> |
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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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```python
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from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier
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model = BigEarthNetv2_0_ImageClassifier.from_pretrained("path_to/huggingface_model_folder")
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```
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e.g.
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```python
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from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier
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model = BigEarthNetv2_0_ImageClassifier.from_pretrained(
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"BIFOLD-BigEarthNetv2-0/BENv2-mixer_b16_224-s1-v0.1.1")
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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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tags:
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- model_hub_mixin
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- pytorch_model_hub_mixin
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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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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 230309492
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version https://git-lfs.github.com/spec/v1
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size 230309492
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