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README.md

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+---
+library_name: pytorch
+tags:
+- chest-xray
+- image-classification
+- pneumonia
+- resnet18
+- computer-vision
+- medical-imaging
+license: mit
+---
+
+# E03 ResNet18 Scheduler - Chest X-Ray Classification
+
+## Overview
+
+This project develops a deep learning image classification model for
+classifying chest X-ray images into two classes:
+
+- NORMAL
+- PNEUMONIA
+
+The model uses a pretrained ResNet18 architecture with transfer learning
+and a StepLR learning-rate scheduler.
+
+This project was developed as part of an AI Engineering Bootcamp final
+portfolio project.
+
+## Model
+
+- Architecture: ResNet18
+- Framework: PyTorch
+- Pretrained weights: ImageNet
+- Number of classes: 2
+- Optimizer: Adam
+- Initial learning rate: 0.0001
+- Batch size: 32
+- Epochs: 5
+- Loss function: CrossEntropyLoss with class weights
+- Scheduler: StepLR
+- Scheduler step size: 2
+- Scheduler gamma: 0.1
+
+## Classes
+
+1. NORMAL
+2. PNEUMONIA
+
+## Test Results
+
+The final E03 model achieved:
+
+| Metric | Score |
+|---|---:|
+| Test Accuracy | 92.31% |
+| Weighted F1-score | 92.16% |
+| Macro F1-score | 91.50% |
+
+### Classification Report
+
+| Class | Precision | Recall | F1-score |
+|---|---:|---:|---:|
+| NORMAL | 0.9697 | 0.8205 | 0.8889 |
+| PNEUMONIA | 0.9014 | 0.9846 | 0.9412 |
+
+### Confusion Matrix
+
+Actual NORMAL: 192 NORMAL, 42 PNEUMONIA
+
+Actual PNEUMONIA: 6 NORMAL, 384 PNEUMONIA
+
+## Methodology
+
+The model was developed using transfer learning with a pretrained
+ResNet18 network. The final fully connected layer was replaced with a
+two-class classification layer.
+
+The training process used:
+
+1. Image preprocessing and dataset preparation
+2. Train/validation/test split
+3. ResNet18 pretrained on ImageNet
+4. Weighted cross-entropy loss
+5. Adam optimizer
+6. StepLR learning-rate scheduling
+7. Five training epochs
+8. Best-model selection based on validation loss
+9. Evaluation on a held-out test set
+
+## Intended Use
+
+This model is intended for educational and experimental purposes,
+particularly for demonstrating computer vision and transfer learning
+workflows.
+
+It should NOT be used as a medical diagnostic system or as a substitute
+for professional medical assessment.
+
+## Limitations
+
+The model was trained and evaluated on a specific chest X-ray dataset.
+Its performance may not generalize to images from different hospitals,
+devices, patient populations, or acquisition protocols.
+
+The model should therefore not be used for clinical decision-making
+without appropriate external validation, clinical testing, regulatory
+review, and professional oversight.
+
+## Model File
+
+The trained model weights are provided in:
+
+E03_ResNet18_Scheduler_best.pth
+
+## Author
+
+Bunsya
+
+AI Engineering Bootcamp - Final Portfolio Project