Update README.md

README.md

@@ -1,16 +1,100 @@
 ---
 license: mit
-language:
-- en
-- id
-metrics:
-- accuracy
-pipeline_tag: image-classification
 tags:
-- vision
-- image-classification
-- pneumonia
-- resnet18
-- pytorch
-- pneumonia-detection
----
+  - image-classification
+  - pneumonia-detection
+  - healthcare
+  - medical-imaging
+  - pytorch
+  - resnet18
+library_name: pytorch
+model_name: resnet18_pneumonia_classifier
+---
+
+# ResNet18 Pneumonia Detection Model
+
+This model is a fine-tuned version of the ResNet18 architecture for pneumonia detection. It was trained on the [Kaggle Chest X-ray Pneumonia dataset](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia), which includes images of normal lungs and lungs with pneumonia. The model is capable of distinguishing between **Pneumonia** and **Normal** chest X-rays.
+
+## Model Details
+
+- **Model Architecture**: ResNet18
+- **Input Size**: 224 x 224
+- **Number of Classes**: 2 (Pneumonia, Normal)
+- **Framework**: PyTorch
+- **Training Dataset**: [Kaggle Chest X-ray Pneumonia Dataset](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia)
+- **Library**: PyTorch
+
+## Model Performance
+
+- **Accuracy**: 83.3%
+- **Loss**: 0.2459
+
+## Intended Use
+
+This model is designed to assist healthcare professionals in identifying pneumonia from chest X-ray images. It should not be used as a sole diagnostic tool but as a supplement to medical expertise.
+
+## Training Details
+
+The model was trained using the following setup:
+
+- **Architecture**: ResNet18 (Pre-trained on ImageNet)
+- **Optimizer**: SGD (Stochastic Gradient Descent)
+  - **Learning Rate**: 0.001
+  - **Momentum**: 0.9
+- **Loss Function**: CrossEntropyLoss
+- **Batch Size**: 32
+- **Data Augmentation**:
+  - Random Rotation (±30 degrees)
+  - Random Zoom (20%)
+  - Random Horizontal Shift (±10% width)
+  - Random Vertical Shift (±10% height)
+  - Random Horizontal Flip
+- **Training Epochs**: 1
+- **Evaluation Metric**: Cross Entropy Loss
+
+### Augmentation Details
+
+The dataset was augmented during training with the following transformations:
+- Randomly rotated some training images by 30 degrees
+- Randomly zoomed some training images by 20%
+- Randomly shifted images horizontally by 10% of the width
+- Randomly shifted images vertically by 10% of the height
+- Randomly flipped images horizontally
+
+
+## How to Use the Model
+
+You can use this model with the `transformers` and `torch` libraries.
+
+```python
+import torch
+from torchvision import transforms
+from PIL import Image
+import requests
+
+# Load the model from Hugging Face Hub
+model = torch.hub.load('huggingface/pytorch', 'resnet18_pneumonia_classifier')
+model.eval()
+
+# Image preprocessing
+transform = transforms.Compose([
+    transforms.Grayscale(num_output_channels=3),
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+
+# Sample Image (replace with your own image)
+url = 'https://storage.googleapis.com/kagglesdsdata/datasets/17810/23812/chest_xray/test/NORMAL/IM-0005-0001.jpeg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=databundle-worker-v2%40kaggle-161607.iam.gserviceaccount.com%2F20240913%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240913T014624Z&X-Goog-Expires=345600&X-Goog-SignedHeaders=host&X-Goog-Signature=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'
+img = Image.open(requests.get(url, stream=True).raw)
+
+# Preprocess the image
+input_img = transform(img).unsqueeze(0)
+
+# Inference
+with torch.no_grad():
+    output = model(input_img)
+    _, predicted = torch.max(output, 1)
+    
+labels = {0: 'Pneumonia', 1: 'Normal'}
+print(f'Predicted label: {labels[predicted.item()]}')