metadata
license: apache-2.0
library_name: pytorch
tags:
- precipitation-nowcasting
- weather-forecasting
- video-transformer
- space-time-attention
- satellite-imagery
pipeline_tag: video-classification
base_model:
- facebook/timesformer-base-finetuned-k400
SaTformer: A Space-Time Transformer for Precipitation Nowcasting
Authors: Levi Harris, Tianlong Chen — The University of North Carolina at Chapel Hill
Usage
import torch
from huggingface_hub import hf_hub_download
from src.model.SaTformer.SaTformer import SaTformer
model = SaTformer(
dim=512,
num_frames=4,
num_classes=64,
image_size=32,
patch_size=4,
channels=11,
depth=12,
heads=8,
dim_head=64,
attn_dropout=0.1,
ff_dropout=0.1,
rotary_emb=False,
attn="ST^2"
)
weights = hf_hub_download(repo_id="leharris3/satformer", filename="sf-64-cls.pt")
model.load_state_dict(torch.load(weights, weights_only=True), strict=False)
model.eval()
with torch.no_grad():
x = torch.rand(1, 4, 11, 32, 32) # (batch, frames, channels, H, W)
logits = model(x) # -> [1, 64]
Citation
@article{harris2025satformer,
title={A Space-Time Transformer for Precipitation Forecasting},
author={Harris, Levi and Chen, Tianlong},
journal={arXiv preprint arXiv:2511.11090},
year={2025}
}