fidoptVM-UNet
VM-UNet, a U-shaped network of Vision Mamba blocks, with its authors' ISIC 2018 checkpoint, run as their test loop runs it: normalised with their ISIC 2018 statistics, resized to 256 x 256, the sigmoid kept above 0.5.
SegmentationCPUversion VM-UNet b87827e, best-vmunet-isic18.pth (sha256 a5b2c175)DERMOSCOPYApache-2.0 (code); weights: no licence stated
Run it
With its KonfAI app, at the benchmark's settings (the fidopt app repositories are private for now: a Hugging Face token with access opens them):
pip install konfai-apps konfai-apps infer fideus-labs/fidopt-segmentation:VMUNet -i image.nii.gz -o out
The app on Hugging Face: its model, its settings and how it is scored.
Results
| Dataset | # | Read by | Score | Failed | Time s |
|---|---|---|---|---|---|
| ISIC 2018, skin lesions outlined on dermoscopy | Dice | 0.868 | 0 | 6 |
- Reference
- Ruan et al., arXiv 2402.02491, 2024