ilex-hub/swift.hcp-sex.1

SwiFT (4D Swin fMRI Transformer, Mamba-free predecessor of NeuroSTORM) -- SwiFT HCP-YA sex-classification fine-tune backbone

Description

SwiFT (Kim, Kwon, Moon, Cha et al., arXiv:2307.05916) is a 4D Swin Transformer for fMRI BOLD volumes. The architecture is the Mamba-free predecessor of NeuroSTORM -- same 4-stage Swin topology (depths [2, 2, 6, 2], channels [36, 72, 144, 288], 4D window [4, 4, 4, 4]) but using conventional WindowAttention4D multi-head self-attention as the per-window mixer instead of NeuroSTORM's Mamba selective-scan SSM.

v0 ships two variants:

• contrastive -- the contrastive-pretraining checkpoint (SimCLR-style backbone trained on a multi-cohort fMRI corpus). Use as a frozen feature extractor for downstream tasks.
• hcp-sex -- the supervised fine-tune on HCP-YA sex classification. Same architecture as contrastive; weights have been further fine-tuned end-to-end. Use as a starting point for additional fine-tuning or as an HCP-specific feature extractor.

The bundle stores only the backbone (SwinTransformer4D) -- the consumer-side SimCLR projection (emb_mlp) and the downstream task heads (clf.head, reg.head) are training- time plumbing and are dropped at extract.

Intended use

HCP-YA sex-classification fine-tuned variant. Same backbone topology as the contrastive variant; the weights have been further fine-tuned on the supervised classification task. Use as an HCP-specific feature extractor OR as initialisation for additional fine-tuning. The classifier head (clf.head) is NOT in the bundle -- add your own downstream MLP on the (288, 2, 2, 2, 20) backbone embedding.

Usage

from ilex.models.swift import SwiFT
model = SwiFT.from_pretrained('ilex-hub/swift.hcp-sex.1')

Authors

Kim P. Y., Kwon J., Moon T., Cha J. (Seoul National University M.IN.D Lab + Connectome Lab)

Citation

Kim P. Y., Kwon J., Moon T., Cha J. et al. (2023). SwiFT -- Swin 4D fMRI Transformer. arXiv 2307.05916.

References

• Kim P. Y., Kwon J., Moon T., Cha J. et al. (2023). SwiFT -- Swin 4D fMRI Transformer. arXiv:2307.05916.
• Liu Z. et al. (2021). Swin Transformer -- Hierarchical Vision Transformer using Shifted Windows. arXiv:2103.14030.
• Upstream code + weights -- github.com/Transconnectome/SwiFT (Apache-2.0).

License

HF Hub license tag: apache-2.0

Effective terms: Apache-2.0 (both upstream code and the in-tree pretrained .ckpt checkpoints at github.com/Transconnectome/SwiFT). The ilex JAX / Equinox port code is separately Apache-2.0 / GPL-3.0.

Upstream license reference: https://www.apache.org/licenses/LICENSE-2.0

Copyright

SwiFT is copyright (c) Transconnectome / Seoul National University 2023, Apache-2.0-licensed on the upstream code + the in-tree pretrained checkpoints (github.com/Transconnectome/SwiFT). The ilex JAX / Equinox port code is separately licensed under Apache-2.0 / GPL-3.0.

Upstream source

Original weights / reference implementation: https://github.com/Transconnectome/SwiFT

Provenance

This artefact was produced by ilex's save/load pipeline. The architecture is implemented in ilex.models.swift.SwiFT and the weights have been converted from their upstream format. See the upstream source above for the canonical reference.