brainmri-ood-hybrid-swint-effb0-noaug

Swin-T + EfficientNet-B0 Hybrid (No Augmentation)

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: not used
• Decision threshold: 0.048

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

Full benchmark

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

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.