README.md

---
thumbnail: "https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png"
tags:
    - resnet50
    - BigEarthNet v2.0
    - Remote Sensing
    - Classification
    - image-classification
    - Multispectral
library_name: configilm
license: mit
widget:
  - 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">

# Resnet50 pretained on BigEarthNet v2.0 using Sentinel-1 & 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-1 & 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)
  - the weights published in this model card were obtained after 23 training epochs
- Batch size: 512
- Learning rate: 0.001
- Dropout rate: 0.15
- Drop Path rate: 0.15
- Learning rate scheduler: LinearWarmupCosineAnnealing for 1000 warmup steps
- Optimizer: AdamW
- Seed: 42

The model was trained using the training script of the 
[official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts).
See details in this repository for more information on how to train the model given the parameters above.

![[BigEarthNet](http://bigearth.net/)](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            |       Value Macro |       Value Micro |
|:------------------|------------------:|------------------:|
| Average Precision |        0.708323 |        0.863369 |
| F1 Score          |        0.652407 |        0.765529 |
| Precision         | 0.717347 | 0.800289 |

# Example
|             Example Input (only RGB bands from Sentinel-2)             |
|:---------------------------------------------------:|
| ![[BigEarthNet](http://bigearth.net/)](example.png) |

| Example Output - Labels                                                   |                                                   Example Output - 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 defines 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` 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/BENv2-resnet50-all-v0.1.1")
```

If you use this model in your research or the provided code, please cite the following papers:
```bibtex
CITATION FOR DATASET PAPER
```
```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}
}
```