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mindgrab
neuroneural/mindgrab
mindgrab is a image segmentation model from neuroneural. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
MindGrab is a MeshNet-based skull-stripping model from the BrainChop project. It takes 256^3 conformed T1 volumes and produces a binary brain mask. The checkpoint runs entirely in tinygrad and powers the in-browser Br…
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Updated Feb 5, 2026
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.pth595 KB · 97%
At a glance
- Task
- Image Segmentation
- License
- mit
- Access
- Public
- Created
- Feb 5, 2026
- Updated
- Feb 5, 2026
- SHA
- c5b4ed5c
Model card
From the Hugging Face model README
MindGrab (BrainChop MeshNet)
MindGrab is a MeshNet-based skull-stripping model from the BrainChop project. It takes 256^3 conformed T1 volumes and produces a binary brain mask. The checkpoint runs entirely in tinygrad and powers the in-browser BrainChop demos (WebGPU/WebGL).
Reference
If you use MindGrab in academic work, please cite the Hugging Face Papers entry 2506.11860, which documents this release and its evaluation context.
Files
| File | Description |
|---|---|
model.json | MeshNet architecture definition (in/out channels, kernel sizes, bias, dropout flags). |
layers.json | Optional Layer configs |
model.pth | FP32 PyTorch checkpoint |
Optionally, you can also run the model through the official frontend at brainchop.org under the name "🪓🧠 omnimodal Skull Stripping"
Usage
uv pip install hf brainchop
from pathlib import Path
from huggingface_hub import hf_hub_download
from brainchop import load, save, api
model_dir = Path(hf_hub_download("neuroneural/mindgrab", "model.json")).parent
hf_hub_download(repo_id, "model.pth")
vol = load("t1_crop.nii.gz")
mask = api.segment(vol, str(model_dir))
save(mask, "mindgrab_mask.nii.gz")
- Task
- Image Segmentation
- License
- mit
- Languages
- en
- Created
- Feb 5, 2026
- Updated
- Feb 5, 2026