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"model_name": "Scale-MAE",
"model_type": "scale_mae",
"architectures": [
"ScaleMAE"
],
"framework": "PyTorch",
"domain": "earth-observation",
"task": "multiscale-geospatial-representation-learning",
"implementation": {
"entry_point": "model/scalemae.py",
"scope": "GSD-aware masked autoencoding with paired low- and high-frequency target reconstruction",
"train_script": "scripts/train.py",
"inference_script": "scripts/inference.py",
"evaluation_script": "scripts/result.py",
"synthetic_data_script": "scripts/fake_data.py"
},
"architecture": {
"family": "scale-aware masked autoencoder",
"input_size": 16,
"target_size": 32,
"patch_size": 4,
"input_patches": 16,
"in_channels": 3,
"embed_dim": 64,
"encoder_depth": 2,
"encoder_heads": 4,
"decoder_dim": 48,
"decoder_depth": 1,
"decoder_heads": 4,
"mask_ratio": 0.75,
"reference_gsd": 1.0,
"blur_kernel": 5,
"band_config": {
"low": {"kernel": 5, "target": "low_frequency"},
"high": {"residual": true, "target": "high_frequency"}
}
},
"data": {
"datasets": [
"FMoW-RGB",
"RESISC-45",
"EuroSAT",
"UCMerced",
"AID",
"MLRSNet"
],
"protocol": "synthetic_multiscale_geospatial_rgb",
"format": "NPZ",
"input_key": "images",
"input_shape": ["N", 3, 16, 16],
"target_key": "targets",
"target_shape": ["N", 3, 32, 32],
"gsd_key": "gsd",
"gsd_shape": ["N"],
"gsd_values": [0.5, 1.0, 2.0, 4.0],
"label_key": "labels",
"label_shape": ["N"],
"num_classes": 4,
"default_train_file": "data/train.npz",
"default_test_file": "data/test.npz"
},
"configuration_sources": [
"conf/config.yaml",
"model/scalemae.py",
"scripts/fake_data.py",
"scripts/train.py",
"scripts/inference.py",
"scripts/result.py"
]
}
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