Upload convnextv2_base-s2-v0.2.0

README.md

@@ -1,109 +1,9 @@
 ---
-thumbnail: "https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png"
 tags:
-    - convnextv2_base
-    - 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: 0.022634
-      - label: Arable land
-        score: 0.047579
-      - label: Beaches, dunes, sands
-        score: 0.012816
-      - label: Broad-leaved forest
-        score: 0.034033
-      - label: Coastal wetlands
-        score: 0.000944
+- model_hub_mixin
+- pytorch_model_hub_mixin
 ---
 
-[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">
-
-# Convnextv2_base 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
-- Learning rate scheduler: LinearWarmupCosineAnnealing for 1000 warmup steps
-- Optimizer: AdamW
-- Seed: 42
-
-The weights published in this model card were obtained after 14 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.
-
-![[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            |       Macro |       Micro |
-|:------------------|------------------:|------------------:|
-| Average Precision |        0.685611 |        0.852548 |
-| F1 Score          |        0.630252 |        0.757458 |
-| Precision         | 0.695574 | 0.783198 |
-
-# Example
-|             A Sentinel-2 image (true color representation)              |
-|:---------------------------------------------------:|
-| ![[BigEarthNet](http://bigearth.net/)](example.png) |
-
-| Class labels                                                              |                                                          Predicted scores |
-|:--------------------------------------------------------------------------|--------------------------------------------------------------------------:|
-| <p> Agro-forestry areas <br> Arable land <br> Beaches, dunes, sands <br> ... <br> Urban fabric </p> | <p> 0.022634 <br> 0.047579 <br> 0.012816 <br> ... <br> 0.016373 </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/convnextv2_base-s2-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}
-}
-```
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Library: [More Information Needed]
+- Docs: [More Information Needed]

model.safetensors

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