kevin-bretz/mindeye2-subj02

MindEye2 — Subject 2 checkpoints

Trained MindEye2 checkpoints for Subject 2 of the Natural Scenes Dataset (NSD). They are the base reconstruction models for the inference-time extensions in kevin-bretz/MindEyeV2: a diffusion timepoint sweep, a VLM caption ablation, and Brain-Optimized Inference (BOI).

These are inference-only checkpoints — each file contains model weights (model_state_dict) only, with optimizer state removed. They contain no NSD data.

Files

Path	Model	Description
finetuned_subj02_1sess_1024hid_low/last.pth	Subject-2 fine-tune	Fine-tuned on 1 session of Subject 2 (hidden_dim=1024, n_blocks=4, blurry_recon=True). This is the model the extensions run on.
multisubject_excludingsubj02/last.pth	Multi-subject pretrain	Pretrained on all NSD subjects except Subject 2; the starting point for the fine-tune above.

Usage

# Fine-tuned Subject-2 model (what the extensions use)
huggingface-cli download kevin-bretz/mindeye2-subj02 \
    finetuned_subj02_1sess_1024hid_low/last.pth --local-dir train_logs

# (optional) the multi-subject pretrain it was fine-tuned from
huggingface-cli download kevin-bretz/mindeye2-subj02 \
    multisubject_excludingsubj02/last.pth --local-dir train_logs

Then run inference (see the repository README):

python recon_inference.py --model_name=finetuned_subj02_1sess_1024hid_low --subj=2 \
    --n_blocks=4 --hidden_dim=1024 --blurry_recon --new_test

Data access & license

The weights are released here; the underlying fMRI/stimulus data is not and must be obtained directly from NSD by agreeing to the NSD Terms & Conditions. Base MindEye2 components (unCLIP decoder, converters) are on pscotti/mindeyev2. Please cite MindEye2, MindEye1, and the Natural Scenes Dataset.