| # ResNet-18 Peripheral Blood Cell Classifier | |
| ## Model Description | |
| This is a ResNet-18 model fine-tuned for peripheral blood cell (PBC) classification using fastai. The model can classify blood cell images into 8 different cell types with 98.07% validation accuracy. | |
| ## Model Details | |
| - **Model Type**: ResNet-18 with transfer learning | |
| - **Framework**: fastai (version <2.8.0) | |
| - **Task**: Image Classification | |
| - **Dataset**: Peripheral Blood Cell (PBC) dataset | |
| - **Classes**: 8 cell types | |
| - **Validation Accuracy**: 98.07% | |
| ## Cell Types | |
| The model can classify the following blood cell types: | |
| 1. **Basophil** - A type of white blood cell involved in inflammatory reactions | |
| 2. **Eosinophil** - White blood cells that fight parasites and allergic reactions | |
| 3. **Erythroblast** - Immature red blood cells | |
| 4. **IG (Immature Granulocyte)** - Immature white blood cells | |
| 5. **Lymphocyte** - White blood cells that fight infections | |
| 6. **Monocyte** - Large white blood cells that become macrophages | |
| 7. **Neutrophil** - Most common white blood cells that fight bacterial infections | |
| 8. **Platelet** - Cell fragments that help blood clotting | |
| ## Training Details | |
| - **Training Images**: 13,674 | |
| - **Validation Images**: 3,418 | |
| - **Architecture**: Pretrained ResNet-18 backbone with custom head | |
| - **Training Strategy**: | |
| - 4 epochs with frozen backbone | |
| - 6 epochs with fine-tuning | |
| - **Input Size**: 224x224 pixels | |
| - **Preprocessing**: Standard ImageNet normalization | |
| ## Performance | |
| - **Validation Accuracy**: 98.07% | |
| - **All cell types**: >95% precision and recall | |
| - **Best performers**: Eosinophil and Platelet (100% precision) | |
| ## Usage | |
| ```python | |
| from fastai.vision.all import * | |
| # Load the model | |
| learn = load_learner('cell_classifier.pkl') | |
| # Predict on an image | |
| pred, pred_idx, probs = learn.predict('path/to/blood_cell_image.jpg') | |
| print(f"Predicted: {pred}") | |
| print(f"Confidence: {probs[pred_idx]:.2%}") | |
| ``` | |
| ## Requirements | |
| ``` | |
| fastai>=2.7.0,<2.8.0 | |
| numpy<2.0 | |
| pillow>=10.0.0 | |
| ``` | |
| ## Model Files | |
| - `cell_classifier.pkl` - Complete fastai learner with model and preprocessing | |
| - `cell_classifier_weights.pth` - PyTorch weights only | |
| - `confusion_matrix.png` - Validation confusion matrix | |
| - `classification_report.csv` - Detailed classification metrics | |
| - `training_summary.json` - Training configuration and results | |
| ## Citation | |
| If you use this model, please cite: | |
| ```bibtex | |
| @misc{pbc-cell-classifier-2024, | |
| title={ResNet-18 Peripheral Blood Cell Classifier}, | |
| author={Your Name}, | |
| year={2024}, | |
| howpublished={Hugging Face Hub}, | |
| url={https://huggingface.co/your-username/pbc-cell-classifier} | |
| } | |
| ``` | |
| ## License | |
| This model is released under the MIT License. | |
| ## Created For | |
| HuggingFace Agents-MCP-Hackathon Track 1 - MCP Tool/Server | |