### **🌺 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!** 🌺🚀