| { |
| "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"] |
| } |
|
|
