metadata
license: cc-by-nc-4.0
datasets:
- clane9/NSD-Flat
Model card for boldgpt_small_patch10.cont
A Vision Transformer (ViT) model trained on BOLD activation maps from NSD-Flat. The training objective was to auto-regressively predict the next patch with shuffled patch order and MSE loss. This model was trained using shared1000 as the held out validation set.
Dependencies
Usage
from boldgpt.data import ActivityTransform
from boldgpt.models import create_model
from datasets import load_dataset
model = create_model("boldgpt_small_patch10.cont", pretrained=True)
dataset = load_dataset("clane9/NSD-Flat", split="train")
dataset.set_format("torch")
transform = ActivityTransform()
batch = dataset[:1]
batch["activity"] = transform(batch["activity"])
# output: (B, N + 1, D) predicted next patches
output, state = model(batch)
Reproducing
Training command:
torchrun --standalone --nproc_per_node=4 \ scripts/train.py \ --out_dir results \ --model boldgpt_small_patch10 \ --no_cat --shuffle --epochs 1000 --bs 512 \ --workers 0 --amp --compile --wandbCommit:
e0b29adc8d5b3ed2f1a555d7de4754ba96a3bb3e