Swin-T + EfficientNet-B0 Hybrid (Augmentation 0.3)

This repository contains one trained checkpoint from Brain MRI Tumor vs No-Tumor - OOD Generalization (10 Models), a comparative course project by Fatih AYIBASAN. It is one item in a 13-checkpoint benchmark covering 10 architectures.

Research and educational use only. Not for clinical diagnosis or medical decision-making.

Project and training notebooks: https://github.com/fatihaybsn/BrainMRI-OOD-10Models
Source revision: a9920408189230b886773a64d113eb35bcba1971
Classes: no_tumor (0), tumor (1)
Architecture: hybrid_swint_effb0
Input size: 224 x 224
Training augmentation: enabled with strength 0.3
Decision threshold: 0.061

Project setting

As documented in the GitHub project, training used 11,500 images from fixed 256 px and 512 px resolution pools. External/OOD evaluation used 3,500 images with varying resolutions from 190 px to 800 px. The goal was to compare how standard, hybrid, and custom architectures generalize under source and resolution shift.

This checkpoint's OOD result

Accuracy	AUC	F1	Recall / Sensitivity	Precision	Cohen's Kappa
0.794958	0.974912	0.748207	0.598792	0.996982	0.592692

Full benchmark

Experiment	Accuracy	AUC	F1	Recall	Precision	Kappa
custom_msaf_effb0_My_model_0.3_augmentation	0.908	0.988	0.901	0.822	0.998	0.817
hybrid_dn121_effb0_0.3_augmentation	0.861	0.967	0.841	0.726	1.000	0.723
hybrid_dn121_effb0_not_augmentation	0.839	0.939	0.812	0.684	1.000	0.680
custom_msaf_effb0_My_model_not_augmentation	0.805	0.936	0.764	0.618	0.999	0.613
hybrid_swinT_effb0_0.3_augmentation (this checkpoint)	0.795	0.975	0.748	0.599	0.997	0.593
resnet34_not_augmentatiton	0.794	0.954	0.747	0.596	0.999	0.591
densenet121	0.785	0.984	0.732	0.578	1.000	0.573
convnext_tiny	0.775	0.960	0.716	0.557	1.000	0.553
hybrid_swinT_effb0_not_augmentation	0.745	0.956	0.665	0.498	1.000	0.494
resnet50_not_augmentatiton	0.719	0.962	0.619	0.448	1.000	0.444
inception_v3_not_augmentation	0.710	0.901	0.602	0.430	1.000	0.426
efficientnet_b0	0.693	0.903	0.568	0.397	0.997	0.392
mobilenetv2_100_not_augmentation	0.639	0.889	0.450	0.290	1.000	0.286

Files and loading

model.safetensors: tensor-only checkpoint converted from the original PyTorch state dict.
config.json: architecture, preprocessing, label mapping, threshold, and provenance metadata.
original_checkpoint.sha256: SHA-256 of the original trained .pt file.
results/: available metrics, reports, thresholds, and result figures for this experiment.

from pathlib import Path
import json
from safetensors.torch import load_file
from modeling import build_model

repo_dir = Path("downloaded-model-directory")
config = json.loads((repo_dir / "config.json").read_text())
model = build_model(config["architecture"], num_classes=2)
model.load_state_dict(load_file(repo_dir / "model.safetensors"), strict=True)
model.eval()

Provenance

Artifact	SHA-256
Original trained `.pt`	`cc3fb44ef79525e56cf8a608740e84a6d602c0d7f0d965bf7e4c9bc8a59bca97`
Published `model.safetensors`	`98bf04110b8447b0f91a0e7a8048942e96eb2755330386357fcae98213115ee1`

The checkpoint, executed notebooks, per-model metrics, result graphics, project report, and Git commit history are published together to provide a traceable record of the training and comparison work.

Limitations

Binary tumor/no-tumor classification only; it does not identify tumor type, location, grade, or prognosis.
Performance was measured on the external/OOD test setup described in the project repository and may not transfer to clinical populations or acquisition protocols.
Dataset bias, source shift, image artifacts, and subject leakage risks can affect performance.
This model has not undergone clinical validation or regulatory review.

Citation

Please cite the GitHub project using its CITATION.cff.

Fatihaybasn/brainmri-ood-hybrid-swint-effb0-aug03