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FEMBA-Tiny-TUAR-BC2
Gunin09/FEMBA-Tiny-TUAR-BC2
FEMBA-Tiny-TUAR-BC2 is a machine learning model from Gunin09. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
Fine-tuned FEMBA-Tiny for TUH Artifact (TUAR) Goal A: predict whether a 5 s EEG window contains artifact activity (official Binary labels, mild class weights).
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.ckpt37.7 MB · 53%
At a glance
- Library
- pytorch
- License
- mit
- Model type
- FEMBA-Tiny
- Access
- Public
- Created
- May 21, 2026
- Updated
- May 21, 2026
- SHA
- 79195efd
Model card
From the Hugging Face model README
FEMBA-Tiny TUAR BC2 — binary artifact per window
Fine-tuned FEMBA-Tiny for TUH Artifact (TUAR) Goal A: predict whether a 5 s EEG window contains artifact activity (official Binary labels, mild class weights).
Files
| File | Description |
|---|---|
model.safetensors | FEMBA weights (load into FEMBA with classification_type=bc) |
pytorch_lightning.ckpt | Original Lightning checkpoint |
config.json | Architecture, normalization, deploy τ |
best_tau_val.json | Val-tuned threshold metadata |
metrics_summary_test.json | Test GATE-BC1 metrics |
Deploy
- Threshold:
deploy_tau = 0.31999999999999995(val-constrained macro-F1 selection) - Decision:
P(ARTIFACT) >= deploy_tau→ artifact - Input: 22-channel bipolar, 5 s @ 200 Hz (1280 samples/ch), quantile norm (see
config.json)
Test metrics (τ from val)
| Metric | Value |
|---|---|
| AUROC | 0.9033453832685784 |
| AUPR | 0.8756412861865515 |
| macro_f1 | 0.8318813512280141 |
| recall_artifact | 0.8524488530688159 |
Training: mild weights [1.0, 1.2173], frozen encoder, 20 epochs, best val_BinaryAUROC at epoch 19.
Load (example)
from pathlib import Path
from safetensors.torch import load_file
# FEMBA class from BioFoundation repo
from models.FEMBA import FEMBA
model = FEMBA(
seq_length=1280, num_channels=22, num_classes=2,
num_blocks=2, embed_dim=35, classification_type="bc",
)
state = load_file("model.safetensors")
model.load_state_dict(state, strict=True)
See FembaTiny-TUAR for preprocessing, inference (run_inference_bc.py), and full result report.
- Library
- pytorch
- Type
- FEMBA-Tiny
- License
- mit
- Created
- May 21, 2026
- Updated
- May 21, 2026