Create README.md

README.md

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+# 🎯 # Image Classification Model for Medical Waste Classification
+
+This is an image classification model trained to classify medical waste into 4 categories, namely cytotoxic, infectious, pathological, and pharmaceutical. The model is based on the Inception v3 architecture and has been adapted to a specific dataset for the task of medical waste classification.
+
+# 🎯 Model Description
+
+The model is based on the Inception v3 architecture with modifications to the fully connected layers for adapting it to the specific image classification task. The architecture consists of a feature extractor followed by a global average pooling layer and fully connected layers with ReLU activation and dropout.
+
+# 🎯 Usage
+
+You can use the model that I have saved in pt format as follows:
+
+```python
+
+import torch
+from torchvision import transforms
+from PIL import Image
+import matplotlib.pyplot as plt
+import numpy as np
+
+def predict_image(image_path, model, transform, class_names):
+    # Load the image
+    image = Image.open(image_path)
+    # Apply transformations
+    image = transform(image).unsqueeze(0)  # Add batch dimension
+
+    # Set the model to evaluation mode
+    model.eval()
+
+    # Make predictions
+    with torch.no_grad():
+        outputs = model(image.to(device))
+        _, predicted = torch.max(outputs, 1)
+        predicted_class = predicted.item()
+        predicted_label = class_names[predicted_class]
+        probabilities = torch.softmax(outputs, dim=1)[0]
+        confidence = probabilities[predicted_class].item()
+        return predicted_class, predicted_label, confidence
+
+# Define transformation to be applied to the input image
+image_transform = transforms.Compose([
+    transforms.Resize((299, 299)),  # Resize to match InceptionV3 input size
+    transforms.ToTensor(),
+    # You can add more transformations such as normalization if needed
+])
+
+# Load the trained model
+model = torch.load('__directory where you save the model__')
+model.to(device)
+
+# Load class names (assuming you have a list of class names)
+class_names = ['cytotoxic', 'infectious', 'pathological', 'pharmaceutical']
+
+# Provide the path to the image you want to predict
+image_path = '__the directory where you store the images you want to classify__'
+
+# Load the true label (assuming you have it)
+true_label = 'pathological'
+
+# Predict the class label
+predicted_class, predicted_label, confidence = predict_image(image_path, model, image_transform, class_names)
+
+# Display the image
+image = Image.open(image_path)
+plt.imshow(np.array(image))
+plt.axis('off')
+plt.title(f'True Class: {true_label} \n Predicted Class: {predicted_label} (Confidence: {confidence*100:.2f}%)')
+plt.show()
+
+```