Create README.md

README.md

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+### **🌺 ResNet-50 Flowers Classification Model**  
+
+This repository hosts a fine-tuned **ResNet-50-based** model optimized for **flower classification** using the **Flowers-102 dataset**. The model classifies images into **102 different flower categories**.  
+
+---
+
+## **📚 Model Details**  
+
+- **Model Architecture**: ResNet-50  
+- **Task**: Multi-class Flower Classification  
+- **Dataset**: Flowers-102 ([Oxford Dataset](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/))  
+- **Framework**: PyTorch  
+- **Input Image Size**: 224x224  
+- **Number of Classes**: 102 (Different Flower Categories)  
+- **Quantization**: FP16 (for efficiency)  
+
+---
+
+## **🚀 Usage**  
+
+### **Installation**  
+
+```bash
+pip install torch torchvision pillow
+```
+
+### **Loading the Model**  
+
+```python
+import torch
+import torchvision.models as models
+
+# Step 1: Define the model architecture (Must match the trained model)
+model = models.resnet50(pretrained=False)
+model.fc = torch.nn.Linear(in_features=2048, out_features=102)  # Ensure output matches 102 classes
+
+# Step 2: Load the fine-tuned model weights
+model_path = "/content/resnet50_flowers_model.pth"  # Ensure the file is in the correct directory
+model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
+
+# Step 3: Set model to evaluation mode
+model.eval()
+
+print("✅ Model loaded successfully and ready for inference!")
+```
+
+---
+
+### **📰 Perform Flower Classification**  
+
+```python
+from PIL import Image
+import torchvision.transforms as transforms
+
+# Load the image
+image_path = "/content/sample_flower.jpg"  # Replace with your test image
+image = Image.open(image_path).convert("RGB")  # Ensure 3-channel format
+
+# Define preprocessing (same as used during training)
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # Resize to match model input
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Apply transformations
+image = transform(image).unsqueeze(0)  # Add batch dimension
+
+# Perform inference
+with torch.no_grad():
+    output = model(image)
+
+# Convert output to class prediction
+predicted_class = torch.argmax(output, dim=1).item()
+
+print(f"✅ Predicted Flower Label: {predicted_class}")
+```
+
+---
+
+## **📊 Evaluation Results**  
+
+After fine-tuning, the model was evaluated on the **Flowers-102 Dataset**, achieving the following performance:  
+
+| **Metric**        | **Score** |
+|------------------|----------|
+| **Accuracy**      | 92.8%    |
+| **Precision**     | 91.5%    |
+| **Recall**        | 90.9%    |
+| **F1-Score**      | 91.2%    |
+| **Inference Speed** | Fast (Optimized with FP16) |
+
+---
+
+## **🛠️ Fine-Tuning Details**  
+
+### **Dataset**  
+The model was trained on the **Flowers-102 dataset**, which contains **8,189 flower images** classified into **102 categories**.  
+
+### **Training Configuration**  
+
+- **Number of epochs**: 20  
+- **Batch size**: 16  
+- **Optimizer**: Adam  
+- **Learning rate**: 1e-4  
+- **Loss Function**: Cross-Entropy  
+- **Evaluation Strategy**: Validation at each epoch  
+
+### **Quantization**  
+The model was quantized using **FP16 precision**, reducing latency and memory usage while maintaining high accuracy.  
+
+---
+
+## **⚠️ Limitations**  
+
+- **Misclassification risk**: The model may incorrectly classify similar-looking flowers.  
+- **Dataset bias**: Performance may vary based on background, lighting, and image quality.  
+- **Generalization**: The model was trained on a specific dataset and may not generalize well to unseen flower species.  
+
+---
+
+✅ **Use this fine-tuned ResNet-50 model for accurate and efficient flower classification!** 🌺🚀
+