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(3.4 M params), pure-bf16, batch=4 at 256³ resolution. Given a 3D
head CT and a transducer placement, predicts the resulting in-skull
pressure field in <1 s on an H100 (≈ 50× faster than the k-Wave
physics simulator the dataset was generated from).relative_l2, did not match
on focal_position_error_mm (~2× worse) or max_pressure_error. This
gap motivated the 5 fine-tune variants in this model collection.| metric | paper | base (this model) | reproduced? |
|---|---|---|---|
relative_l2 mean ± std | 0.414 ± 0.086 | 0.384 ± 0.078 | ✅ Yes (slightly beats paper) |
relative_l2 median | 0.394 | 0.369 | ✅ |
focal_position_error_mm mean ± std | 2.89 ± 2.14 | 6.49 ± 4.58 | ❌ No (~2.25× worse mean) |
focal_position_error_mm median | 2.45 | 5.15 | ❌ |
max_pressure_error mean ± std | 0.199 ± 0.158 | 0.225 ± 0.116 | ✅ Yes (within paper's std) |
max_pressure_error median | 0.166 | 0.217 | (slightly above paper) |
focal_pressure_error median | : | 0.528 | : |
focal_iou_fwhm median | : | 0.143 | : |
inference_latency_s (b=1, H100) | 11.4 (RTX 4090) | 0.233 | 49× faster (different HW) |
1from huggingface_hub import hf_hub_download
2import torch
3
4ckpt = torch.load(
5 hf_hub_download("masonwang025/deeptfus-base", "ckpt_best.pt"),
6 map_location="cpu", weights_only=False,
7)
8# ckpt['model'] : state_dict for the model defined in masonwang025/deeptfus repo
9# ckpt['config'] : training config (architecture knobs + train hyperparams)
10# ckpt['epoch'] : 43 (best by val_rel_l2)