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43dce84 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | {
"model_name": "DINCAE",
"model_type": "dincae",
"architectures": ["DINCAE"],
"framework": "PyTorch",
"domain": "ocean",
"task": "probabilistic-sst-gap-reconstruction",
"implementation": {
"entry_point": "model/dincae.py",
"scope": "core-method reduced-spatial-scale engineering reproduction with paper dimensions recorded separately",
"train_script": "scripts/train.py",
"inference_script": "scripts/inference.py",
"evaluation_script": "scripts/result.py",
"synthetic_data_script": "scripts/fake_data.py"
},
"architecture": {
"engineering_input_shape": ["B", 10, 32, 32],
"engineering_output_shape": ["B", 2, 32, 32],
"paper_input_shape": ["B", 10, 112, 112],
"paper_output_shape": ["B", 2, 112, 112],
"engineering_filters": [8, 12, 18, 27],
"paper_filters": [16, 24, 36, 54],
"paper_bottleneck": [2646, 529, 2646],
"decoder": "nearest-neighbor upsampling with encoder skips"
},
"data": {
"dataset": "AVHRR Pathfinder daily SST",
"format_version": "dincae_avhrr_v1",
"paper_period": "1985-2009",
"paper_time_steps": 5266,
"paper_grid": [112, 112],
"engineering_grid": [32, 32],
"input_channels": 10,
"output_channels": 2,
"synthetic": true
},
"configuration_sources": [
"conf/config.yaml",
"model/dincae.py",
"scripts/fake_data.py",
"scripts/train.py",
"scripts/inference.py",
"scripts/result.py"
]
}
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