MindEye2

Installation

1. Git clone this repository:

git clone https://github.com/MedARC-AI/MindEyeV2.git

2. Download https://huggingface.co/datasets/pscotti/mindeyev2 and place them in the same folder as your git clone. Warning: This will download over 120 GB of data! You may want to only download some parts of the huggingface dataset (e.g., not all the pretrained models contained in "train_logs")

cd MindEyeV2
git clone https://huggingface.co/datasets/pscotti/mindeyev2 .

or for specifically downloading only parts of the dataset (will need to edit depending on what you want to download):

from huggingface_hub import snapshot_download, hf_hub_download
snapshot_download(repo_id="pscotti/mindeyev2", repo_type = "dataset", revision="main", allow_patterns="*.tar",
    local_dir= "your_local_dir", local_dir_use_symlinks = False, resume_download = True)
hf_hub_download(repo_id="pscotti/mindeyev2", filename="coco_images_224_float16.hdf5", repo_type="dataset")

3. Run . src/setup.sh to install a new "fmri" virtual environment. Make sure the virtual environment is activated with "source fmri/bin/activate".

Usage

• src/Train.ipynb trains models (both single-subject and multi-subject). Check the argparser arguments to specify how you want to train the model (e.g., --num_sessions=1 to train with 1-hour of data).
  • Final models used in the paper were trained on an 8xA100 80GB node and will OOM on weaker compute. You can train the model on weaker compute with minimal performance impact by changing certain model arguments: We recommend lowering hidden_dim to 1024 (or even 512), removing the low-level submodule (--no-blurry_recon), and lowering the batch size.
  • To train a single-subject model, set --no-multi_subject and --subj=# where # is the subject from NSD you wish to train
  • To train a multi-subject model (i.e., pretraining), set --multi_subject and --subj=# where # is the one subject out of 8 NSD subjects to not include in the pretraining.
  • To fine-tune from a multi-subject model, set --no-multi_subject and --multisubject_ckpt=path_to_your_pretrained_ckpt_folder
• src/recon_inference.ipynb will run inference on a pretrained model, outputting tensors of reconstructions/predicted captions/etc.
• src/final_evaluations.ipynb will visualize reconstructions output from src/recon_inference and compute quantitative metrics.
• See .slurm files for example scripts for running the .ipynb notebooks as batch jobs submitted to Slurm job scheduling.