Update README.md

README.md

@@ -21,22 +21,22 @@ tags:
 license: apache-2.0
 pipeline_tag: image-classification
 base_model:
-- galeio-research/nereus-sar-1
+- galeio-research/OceanSAR-1
 ---
 
-# Model Card for Nereus-SAR-1-TenGeoP
+# Model Card for OceanSAR-1-TenGeoP
 
 ## Model Details
 
 ### Model Description
 
-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.
+OceanSAR-1-TenGeoP is a linear probing head for classifying ocean geophysical phenomena, built on top of the OceanSAR-1 foundation model. It leverages the powerful features extracted by OceanSAR-1 to accurately identify 10 different geophysical phenomena in Synthetic Aperture Radar (SAR) imagery.
 
 - **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)
+- **Base model:** OceanSAR-1 (ResNet50/ViT variants)
 - **Training data:** Sentinel-1 Wave Mode (WV) SAR images with labeled geophysical phenomena
 
 ## Uses
@@ -68,14 +68,14 @@ import torch
 from transformers import AutoModelForImageClassification
 
 # Load the foundation model and classification head
-nereus = AutoModelForImageClassification.from_pretrained("galeio-research/nereus-sar-1-tengeo")
+oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-tengeop")
 
 # Prepare your SAR image (should be single-channel VV polarization)
 dummy_image = torch.randn(1, 1, 256, 256)  # (B, C, H, W)
 
 # Extract features and classify geophysical phenomena
 with torch.no_grad():
-    outputs = nereus(dummy_image)
+    outputs = oceansar(dummy_image)
     predicted_class = torch.argmax(outputs.logits, dim=1).item()
 ```
 
@@ -86,7 +86,7 @@ with torch.no_grad():
 - **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
+- **Preprocessing:** Same as base OceanSAR-1 model
 
 ## Evaluation
 
@@ -120,11 +120,11 @@ The model outperforms existing approaches:
 
 - PyTorch >= 1.8.0
 - Transformers >= 4.30.0
-- Base Nereus-SAR-1 model
+- Base OceanSAR-1 model
 
 ### Input Specifications
 
-- Same as base Nereus-SAR-1 model
+- Same as base OceanSAR-1 model
 - Single channel (VV polarization) SAR images
 - 256x256 pixel resolution