README.md

metadata

license: apache-2.0
library_name: pytorch
pipeline_tag: image-segmentation
tags:
  - fsot
  - unet
  - cell-detection
  - zero-shot
  - biomedical

Wavegazer Net

Seed-derived visual U (FSOT) for segmentation and cell centroid detect. Zero trainable weights on the spine. Pin D1D38A.

This is not FlowNet (optical flow, ICCV 2015).

Use

import torch
from wavegazer import WavegazerNet

net = WavegazerNet(in_channels=1, n_classes=2, sparse=True).eval()
x = torch.rand(1, 1, 256, 256)
logits = net(x)                 # dense (N, K, H, W)
peaks = net.detect(x)           # centroids, 7 µm Biohub protocol

Frozen codon kernels load with the module; optional wavegazer_buffers.pt is the same buffers serialized.

Benchmarks (honest)

• Dense square unit test: Dice > 0.7
• Synthetic disks vs trained U-Net: U-Net wins (fitted); Wavegazer is closed-form
• Biohub detect @ 7 µm (16 volumes, YX): see artifacts/biohub_peaks_7um.json

Not a CellMot 0.848 claim. Detect gate first, linking later.

Code: GitHub dappalumbo91/Wavegazer-Net