bshepp/3body-l4-log
222 MB
Brian Sheppard PRO
bshepp
AI & ML interests
Reinforcement Learning, Computer VIsion, Fundimental Mathematics, Fundimental AI architecture, AI Safety(for the AI), helping people
Recent Activity
updated a dataset 7 days ago
bshepp/residuals-fingerprints published a dataset 10 days ago
bshepp/residuals-fingerprints updated a dataset 13 days ago
bshepp/staring-into-the-void-runsOrganizations
Posts 2
Post
207
Scored 0.390/0.409 on the Vesuvius Challenge Surface Detection — the 1st place team hit 0.614 with nnU-Net. That gap sent us down a rabbit hole.
We built four independent 3D segmentation pipelines to understand what actually matters: standard U-Net, deep supervision + focal loss, multi-scale fusion, and finally nnU-Net itself. Each pipeline taught us something — V1 showed us our GPU was idle 90% of the time (CPU feature bottleneck), V2 proved skeleton recall beats clDice, V3 NaN'd at epoch 55 from FP16 overflow, and nnU-Net is training on HF Jobs right now.
The 1st place post-processing pipeline (binary closing, height-map patching, LUT hole plugging) turned out to be a bigger lever than we expected. We only had access after the competition closed and writeups were published.
Code: github.com/bshepp/volumen
We built four independent 3D segmentation pipelines to understand what actually matters: standard U-Net, deep supervision + focal loss, multi-scale fusion, and finally nnU-Net itself. Each pipeline taught us something — V1 showed us our GPU was idle 90% of the time (CPU feature bottleneck), V2 proved skeleton recall beats clDice, V3 NaN'd at epoch 55 from FP16 overflow, and nnU-Net is training on HF Jobs right now.
The 1st place post-processing pipeline (binary closing, height-map patching, LUT hole plugging) turned out to be a bigger lever than we expected. We only had access after the competition closed and writeups were published.
Code: github.com/bshepp/volumen