Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- resnet
+- SAR
+- RADAR
+- EO
+- classification
+- linear-probe
+- sentinel-1
+- ocean
+- tengeop
+license: apache-2.0
+pipeline_tag: image-classification
+base_model:
+- galeio-research/nereus-sar-1
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for Nereus-SAR-1-TenGeoP
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+Nereus-SAR-1-TenGeoP is a linear probing head for classifying ocean geophysical phenomena, built on top of the Nereus-SAR-1 foundation model. It leverages the powerful features extracted by Nereus-SAR-1 to accurately identify 10 different geophysical phenomena in Synthetic Aperture Radar (SAR) imagery.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** Thomas Kerdreux, Alexandre Tuel @ [Galeio](http://galeio.fr)
+- **Deployed by:** Antoine Audras @ [Galeio](http://galeio.fr)
+- **Model type:** Linear Classification Head on Vision Foundation Model
+- **License:** Apache License 2.0
+- **Base model:** Nereus-SAR-1 (ResNet50/ViT variants)
+- **Training data:** Sentinel-1 Wave Mode (WV) SAR images with labeled geophysical phenomena
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is designed for automated classification of geophysical phenomena in SAR imagery over ocean surfaces. It can be used for:
 
-## Bias, Risks, and Limitations
+- Rapid identification of ocean features in SAR data
+- Monitoring of maritime environments
+- Automated analysis of large SAR datasets
+- Ocean science and research applications
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Performance Results
 
-[More Information Needed]
+The model achieves state-of-the-art performance on TenGeoP classification, with performance varying by backbone architecture:
 
-### Recommendations
+| Backbone | TenGeoP Accuracy (%) |
+|----------|---------------------|
+| ResNet50 | 75.5 |
+| ViT-S/16 | 78.6 |
+| ViT-S/8  | 82.1 |
+| ViT-B/8  | 83.6 |
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## How to Use
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+```python
+import torch
+from transformers import AutoModelForImageClassification
 
-## How to Get Started with the Model
+# Load the foundation model and classification head
+nereus = AutoModelForImageClassification.from_pretrained("galeio-research/nereus-sar-1-tengeo")
 
-Use the code below to get started with the model.
+# Prepare your SAR image (should be single-channel VV polarization)
+dummy_image = torch.randn(1, 1, 256, 256)  # (B, C, H, W)
 
-[More Information Needed]
+# Extract features and classify geophysical phenomena
+with torch.no_grad():
+    outputs = nereus(dummy_image)
+    predicted_class = torch.argmax(outputs.logits, dim=1).item()
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Dataset:** Sentinel-1 Wave Mode (WV) SAR images with labeled geophysical phenomena
+- **Labels:** 10 classes of ocean geophysical phenomena
+- **Size:** Balanced dataset across all classes
+- **Preprocessing:** Same as base Nereus-SAR-1 model
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+### Metrics
 
-#### Factors
+TenGeoP classification performance is evaluated using accuracy (%), achieving:
+- 75.5% accuracy with ResNet50 backbone
+- 78.6% accuracy with ViT-S/16 backbone
+- 82.1% accuracy with ViT-S/8 backbone
+- 83.6% accuracy with ViT-B/8 backbone
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+### Comparison to Other Backbones
 
-[More Information Needed]
+The model outperforms existing approaches:
+- CROMA (ViT-B/8): 65.4% accuracy
+- MoCo (ResNet50): 60.9% accuracy
+- DeCUR (ResNet50): 58.3% accuracy
+- DOFA (ViT-B/16): 58.4% accuracy
+- DOFA (ViT-L/16): 63.4% accuracy
+- SoftCon (ViT-S/14): 73.2% accuracy
+- SoftCon (ViT-B/14): 74.8% accuracy
 
-#### Metrics
+## Technical Specifications
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+### Hardware Requirements
 
-[More Information Needed]
+- Same as base model
+- Minimal additional computational cost for inference
 
-### Results
+### Dependencies
 
-[More Information Needed]
+- PyTorch >= 1.8.0
+- Transformers >= 4.30.0
+- Base Nereus-SAR-1 model
 
-#### Summary
+### Input Specifications
 
+- Same as base Nereus-SAR-1 model
+- Single channel (VV polarization) SAR images
+- 256x256 pixel resolution
 
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```bibtex
+@article{kerdreux2025efficientselfsupervisedlearningearth,
+  title={Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation},
+  author={Kerdreux, Thomas and Tuel, Alexandre and Febvre, Quentin and Mouche, Alexis and Chapron, Bertrand},
+  journal={arXiv preprint arXiv:2504.06962},
+  year={2025},
+  eprint={2504.06962},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2504.06962},
+}
+```
+
+## Acknowledgements
+
+This work was granted access to the HPC resources of IDRIS and TGCC under the allocation 2025-[A0171015666] made by GENCI.