File size: 1,461 Bytes
8b64ae3 | 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 | {
"model_name": "SmaAtUNet",
"model_type": "smaatunet",
"architectures": ["SmaAtUNet", "CBAM", "DepthwiseSeparableConv"],
"framework": "PyTorch",
"domain": "atmosphere",
"task": "precipitation-nowcasting",
"implementation": {
"entry_point": "model/smaatunet.py",
"scope": "small attention U-Net with depthwise-separable convolutions for multi-step precipitation regression",
"train_script": "scripts/train.py",
"inference_script": "scripts/inference.py",
"evaluation_script": "scripts/result.py",
"synthetic_data_script": "scripts/fake_data.py"
},
"architecture": {
"family": "five-level attention U-Net",
"in_channels": 12,
"out_channels": 6,
"base_channels": 8,
"kernels_per_layer": 2,
"reduction_ratio": 4,
"bilinear": true,
"attention": "channel and spatial CBAM",
"convolution": "depthwise separable"
},
"data": {
"datasets": ["KNMI precipitation radar maps"],
"protocol": "smaat_unet_synthetic_precip_v1",
"format": "NPZ",
"train_file": "data/train.npz",
"test_file": "data/test.npz",
"input_shape": ["N", 12, 288, 288],
"target_shape": ["N", 6, 288, 288],
"interval_minutes": 5,
"forecast_horizon_minutes": 30,
"required_metadata": ["format_version", "data_source"]
},
"configuration_sources": ["conf/config.yaml", "model/smaatunet.py", "scripts/fake_data.py", "scripts/train.py", "scripts/inference.py", "scripts/result.py"]
}
|