Pre-trained Weights

from torchgeo.models import resnet50, ResNet50_Weights

# Method 1: Direct weight enum
weights = ResNet50_Weights.SENTINEL2_ALL_MOCO
model = resnet50(weights=weights)

# Method 2: Using get_weight
from torchgeo.models import get_weight
weight = get_weight('ResNet50_Weights.SENTINEL2_ALL_MOCO')
model = resnet50(weights=weight)

weights = ResNet50_Weights.SENTINEL2_ALL_MOCO

print(f"Dataset: {weights.meta['dataset']}")
print(f"Channels: {weights.meta['in_chans']}")
print(f"SSL Method: {weights.meta['ssl_method']}")
print(f"Bands: {weights.meta['bands']}")

# Output:
# Dataset: SSL4EO-S12
# Channels: 13
# SSL Method: moco
# Bands: ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8a', 'B9', 'B10', 'B11', 'B12']

from torchgeo.models import get_model_weights

# Get all weights for a model
weights = get_model_weights('resnet50')
for w in weights:
    print(f"{w.name}: {w.meta.get('in_chans')} channels")

weights = ResNet50_Weights.SENTINEL2_ALL_MOCO
transforms = weights.transforms

# Apply transforms to input
output = transforms(input_tensor)

<SENSOR>_<BANDS>_<METHOD>