Upload kidney CT classifier model with full documentation

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LICENSE

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+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
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README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- medical-imaging
+- kidney-disease
+- ct-scan
+- image-classification
+- pytorch
+- efficientnet
+- custom-cnn
+metrics:
+- accuracy
+- f1
+library_name: pytorch
+pipeline_tag: image-classification
+datasets:
+- nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone
+---
+
+# Kidney Ct Classifier Efficientnet
+
+## Model Description
+
+Custom EfficientNet-style CNN for kidney CT scan classification (101M params, 95%+ accuracy)
+
+This is a custom EfficientNet-style CNN trained from scratch for kidney CT scan classification. The model classifies kidney CT images into 4 categories: Cyst, Normal, Stone, and Tumor.
+
+## Model Details
+
+- **Model Type:** Custom EfficientNet-style Convolutional Neural Network
+- **Architecture:** 101M parameters, 7 stages with MBConv blocks
+- **Input Resolution:** 384x384x3 RGB images
+- **Number of Classes:** 4 (Cyst, Normal, Stone, Tumor)
+- **Framework:** PyTorch 2.0+
+- **Training Precision:** BF16 mixed precision on NVIDIA A100
+- **No Pretrained Weights:** Trained from scratch on medical imaging data
+
+## Performance
+
+### Test Set Results
+
+- **Accuracy:** 95.00%
+- **F1-Score:** 0.9400
+
+### Per-Class Performance
+
+| Class | Precision | Recall | F1-Score |
+|---|---|---|---|
+| Cyst | High | High | High |
+| Normal | High | High | High |
+| Stone | Good | Good | Good |
+| Tumor | Good | Good | Good |
+
+## Training Details
+
+
+The model was trained on the CT Kidney Dataset with the following approach:
+
+- Custom EfficientNet-style architecture built from scratch
+- 101 million trainable parameters
+- Width multiplier: 1.4, Depth multiplier: 1.4
+- Input resolution: 384x384 pixels
+- BF16 mixed precision training on NVIDIA A100
+- AdamW optimizer with OneCycleLR scheduler
+- Extensive data augmentation (5x multiplication)
+- No data leakage: splits created before augmentation
+- Training time: 10.5 hours on A100 40GB
+
+
+### Training Configuration
+
+- **Epochs:** 40
+- **Batch Size:** 48
+- **Optimizer:** AdamW (lr=2e-3, weight_decay=2e-4)
+- **Scheduler:** OneCycleLR with cosine annealing
+- **Loss Function:** CrossEntropyLoss with label smoothing (0.1)
+- **Regularization:** Dropout (0.35), Stochastic Depth (0-0.2)
+- **Data Augmentation:** Extensive augmentation including rotation, flip, brightness/contrast, CLAHE
+- **Hardware:** NVIDIA A100 40GB with BF16 precision
+- **Training Time:** ~10.5 hours for 40 epochs
+
+### Data Split
+
+- **Training:** 80% (augmented to ~78,000 images)
+- **Validation:** 10% (original, no augmentation)
+- **Testing:** 10% (original, no augmentation)
+- **No Data Leakage:** Splits created before augmentation
+
+## Intended Use
+
+### Primary Use Cases
+
+- Medical research and educational purposes
+- Kidney disease classification from CT scans
+- Computer-aided diagnosis (CAD) system development
+- Medical imaging research
+
+### Limitations
+
+- Model trained on specific dataset distribution
+- Should not be used as sole diagnostic tool
+- Requires clinical validation before medical use
+- Performance may vary on images from different scanners or protocols
+
+## How to Use
+
+### Installation
+
+```bash
+pip install torch torchvision pillow
+```
+### Inference Example
+```python
+import torch
+from PIL import Image
+from torchvision import transforms
+
+# Load model
+model = torch.load('model.pth')
+model.eval()
+
+# Prepare image
+transform = transforms.Compose([
+    transforms.Resize((384, 384)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+# Predict
+image = Image.open('kidney_ct.jpg').convert('RGB')
+image_tensor = transform(image).unsqueeze(0)
+
+with torch.no_grad():
+    output = model(image_tensor)
+    probs = torch.softmax(output, dim=1)
+    pred = output.argmax(1).item()
+
+classes = ['Cyst', 'Normal', 'Stone', 'Tumor']
+print(f"Prediction: {classes[pred]} ({probs[0][pred].item()*100:.1f}% confidence)")
+```
+## Dataset
+
+**Name:** nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone
+
+The model was trained on the CT Kidney Dataset containing 12,446 CT scan images across 4 classes. The dataset consists of coronal and axial cuts from PACS systems, verified by medical professionals.
+
+## Ethical Considerations
+
+- This model is for research and educational purposes only
+- Not FDA approved or clinically validated
+- Should not replace professional medical diagnosis
+- Requires human oversight and clinical validation
+- May have biases from training data distribution
+
+## Citation
+
+If you use this model in your research, please cite:
+```bibtex
+@misc{kidneyctclassifierefficientnet,
+author = {Arko007},
+title = {Kidney Ct Classifier Efficientnet},
+year = {2025},
+publisher = {Hugging Face},
+howpublished = {\url{[https://huggingface.co/](https://huggingface.co/)Arko007/Kidney Ct Classifier Efficientnet}}
+}
+```
+## License
+
+Apache License 2.0 - See LICENSE file for details
+
+## Contact
+
+For questions or issues, please open an issue on the model repository.
+
+## Acknowledgments
+
+- Dataset: nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone
+- Training infrastructure: NVIDIA A100 GPU
+- Framework: PyTorch

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