OceanSAR 1 Wind

Name: OceanSAR 1 Wind
Author: galeio-research

license:apache-2.0

galeio-research

Image Model

OTHER

2504.06962B params

New

4 downloads

Early-stage

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Edge AI:

Mobile

Laptop

Server

5598GB+ RAM

Mobile

Laptop

Server

Quick Summary

OceanSAR-1-Wind is a linear probing head for wind speed prediction built on top of the OceanSAR-1 foundation model.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

2333GB+ RAM

Code Examples

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

How to Usepythontransformers

import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s

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