TerraFM

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license:apache-2.0
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Quick Summary

AI model with specialized capabilities.

Code Examples

🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")
🛠 Usagepythonpytorch
from terrafm import terrafm_base, terrafm_large
import torch

# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)

# Load TerraFM-Base model
model = terrafm_base()

# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)

# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")

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