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protocol: synthetic_sevir
# Reference dataset: official SEVIR VIL (earthformer_sevir_v1.yaml) uses
# 384x384 frames at 5-minute intervals, seq_len = 25 (13 input + 12 output),
# "sequent" sampling with stride 12, single channel. The small spatial shape
# below is intentionally the default for CPU smoke verification.
input_length: 13
output_length: 12
height: 32
width: 32
channels: 1
frame_interval_minutes: 5
normalization: unit
data_dir: data/synthetic_sevir
train_samples: 8
val_samples: 2
test_samples: 2
train_npz: data/synthetic_sevir/train.npz
val_npz: data/synthetic_sevir/val.npz
test_npz: data/synthetic_sevir/test.npz
fallback_if_missing: true
model:
dims: [4, 8]
depths: [1, 1]
heads: 1
pattern: [[2, 4, 4]]
num_global_vectors: 1
ff_ratio: 2.0
dropout: 0.0
train:
seed: 42
# Training always starts from a randomly initialized model (from scratch).
# The --resume flag only restores a locally trained checkpoint for continued
# training; no official pretrained Earthformer weights are ever downloaded.
batch_size: 1
epochs: 5
validation_steps: 1
# Per-epoch validation computes only cheap MSE/MAE (the training loss is
# already MSE). Enable this to also compute SSIM/CSI every epoch; the
# authoritative full evaluation lives in script/result.py.
compute_full_metrics: false
learning_rate: 0.001
weight_decay: 0.00001
# Trained weights for inference are saved here as earthformer.pt.
output_dir: data/checkpoint
dataloader:
num_workers: 0
pin_memory: false
distributed:
backend: auto
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