{
  "model_name": "SatMAE++",
  "model_type": "satmae_pp",
  "architectures": [
    "SatMAEPP"
  ],
  "framework": "PyTorch",
  "domain": "earth-observation",
  "task": "remote-sensing-representation-learning",
  "implementation": {
    "entry_point": "model/satmaepp.py",
    "scope": "masked autoencoding with convolutional native-scale reconstruction for RGB or grouped multispectral satellite imagery",
    "train_script": "scripts/train.py",
    "inference_script": "scripts/inference.py",
    "evaluation_script": "scripts/result.py",
    "synthetic_data_script": "scripts/fake_data.py"
  },
  "architecture": {
    "family": "multiscale masked autoencoder with convolutional upsampling",
    "runtime_profile": "smoke",
    "mode": "rgb",
    "image_size": 32,
    "patch_size": 4,
    "in_channels": 3,
    "embed_dim": 64,
    "encoder_depth": 2,
    "encoder_heads": 4,
    "decoder_dim": 32,
    "decoder_depth": 1,
    "decoder_heads": 4,
    "mask_ratio": 0.75,
    "scales": [1, 2],
    "spectral_groups": null,
    "spatial_mask": false,
    "norm_pix_loss": false,
    "proj_ratio": 4
  },
  "data": {
    "datasets": [
      "FMoW-RGB",
      "FMoW-Sentinel",
      "EuroSAT",
      "UCMerced",
      "RESISC-45"
    ],
    "protocol": "fmow_rgb_satmaepp",
    "format": "NPZ",
    "input_key": "images",
    "input_shape": ["N", 3, 32, 32],
    "native_target_keys": ["images_2x"],
    "native_target_shapes": [["N", 3, 64, 64]],
    "label_key": "labels",
    "label_shape": ["N"],
    "num_classes": 10,
    "default_train_file": "data/train.npz",
    "default_test_file": "data/test.npz",
    "required_metadata": ["protocol", "data_source"]
  },
  "configuration_sources": [
    "conf/config.yaml",
    "model/satmaepp.py",
    "scripts/fake_data.py",
    "scripts/train.py",
    "scripts/inference.py",
    "scripts/result.py"
  ]
}