| ---
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| library_name: timm
|
| pipeline_tag: image-classification
|
| license: mit
|
| tags:
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| - pytorch
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| - safetensors
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| - timm
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| - image-classification
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| - brain-mri
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| - tumor-detection
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| - out-of-distribution
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| - medical-imaging
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| - benchmark
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| metrics:
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| - accuracy
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| - roc_auc
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| - f1
|
| ---
|
|
|
| # EfficientNet-B0 (OOD Brain MRI)
|
|
|
| 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](https://github.com/fatihaybsn/BrainMRI-OOD-10Models)
|
| - Source revision: [`a9920408189230b886773a64d113eb35bcba1971`](https://github.com/fatihaybsn/BrainMRI-OOD-10Models/commit/a9920408189230b886773a64d113eb35bcba1971)
|
| - Classes: `no_tumor` (0), `tumor` (1)
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| - Architecture: `efficientnet_b0`
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| - Input size: `224 x 224`
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| - Training augmentation: not used
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| - Decision threshold: `0.5`
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|
|
| ## 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 |
|
| |---:|---:|---:|---:|---:|---:|
|
| | 0.692591 | 0.902756 | 0.567848 | 0.396979 | 0.996965 | 0.391525 |
|
|
|
| ## 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 |
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| | hybrid_dn121_effb0_0.3_augmentation | 0.861 | 0.967 | 0.841 | 0.726 | 1.000 | 0.723 |
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| | hybrid_dn121_effb0_not_augmentation | 0.839 | 0.939 | 0.812 | 0.684 | 1.000 | 0.680 |
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| | custom_msaf_effb0_My_model_not_augmentation | 0.805 | 0.936 | 0.764 | 0.618 | 0.999 | 0.613 |
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| | hybrid_swinT_effb0_0.3_augmentation | 0.795 | 0.975 | 0.748 | 0.599 | 0.997 | 0.593 |
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| | resnet34_not_augmentatiton | 0.794 | 0.954 | 0.747 | 0.596 | 0.999 | 0.591 |
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| | densenet121 | 0.785 | 0.984 | 0.732 | 0.578 | 1.000 | 0.573 |
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| | convnext_tiny | 0.775 | 0.960 | 0.716 | 0.557 | 1.000 | 0.553 |
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| | hybrid_swinT_effb0_not_augmentation | 0.745 | 0.956 | 0.665 | 0.498 | 1.000 | 0.494 |
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| | resnet50_not_augmentatiton | 0.719 | 0.962 | 0.619 | 0.448 | 1.000 | 0.444 |
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| | inception_v3_not_augmentation | 0.710 | 0.901 | 0.602 | 0.430 | 1.000 | 0.426 |
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| | **efficientnet_b0 (this checkpoint)** | 0.693 | 0.903 | 0.568 | 0.397 | 0.997 | 0.392 |
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| | 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.
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| - `config.json`: architecture, preprocessing, label mapping, threshold, and provenance metadata.
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| - `original_checkpoint.sha256`: SHA-256 of the original trained `.pt` file.
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| - `results/`: available metrics, reports, thresholds, and result figures for this experiment.
|
|
|
| ```python
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| from pathlib import Path
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| import json
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| from safetensors.torch import load_file
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| from modeling import build_model
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|
|
| repo_dir = Path("downloaded-model-directory")
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| config = json.loads((repo_dir / "config.json").read_text())
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| model = build_model(config["architecture"], num_classes=2)
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| model.load_state_dict(load_file(repo_dir / "model.safetensors"), strict=True)
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| model.eval()
|
| ```
|
|
|
| ## Provenance
|
|
|
| | Artifact | SHA-256 |
|
| |---|---|
|
| | Original trained `.pt` | `f097f292733603a5030e74b5398e333633c8055b9666871e376f67493f2475fc` |
|
| | Published `model.safetensors` | `ea7802bee8ce10223ed169540513cdccc4865218aeb1cd49f8f541ae9cf30a75` |
|
|
|
| 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.
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| - Performance was measured on the external/OOD test setup described in the project repository and may not transfer to clinical populations or acquisition protocols.
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| - Dataset bias, source shift, image artifacts, and subject leakage risks can affect performance.
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| - This model has not undergone clinical validation or regulatory review.
|
|
|
| ## Citation
|
|
|
| Please cite the GitHub project using its [`CITATION.cff`](https://github.com/fatihaybsn/BrainMRI-OOD-10Models/blob/main/CITATION.cff).
|
| |
