README.md

---
library_name: pytorch
tags:
- image-classification
- pytorch
- efficientnet
- flowers
- computer-vision
- oxford-flowers-102
- vision
pipeline_tag: image-classification
datasets:
- dpdl-benchmark/oxford_flowers102
license: mit
metrics:
- accuracy
base_model: efficientnet
language:
- en
---

# 🌸 Flower Classification Model

## 📊 Model Info

[![HF Model](https://img.shields.io/badge/🤗-Model%20Hub-yellow)](https://huggingface.co/nailarais1/image-classifier-efficientnet)  
[![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)  
[![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-red.svg)](https://pytorch.org)  
[![Downloads](https://img.shields.io/badge/Downloads-100+-blue)](https://huggingface.co/nailarais1/image-classifier-efficientnet)  

Model: nailarais1/image-classifier-efficientnet  
Author: Naila Rais  
Task: Image Classification · 102 Flower Species  

## Quick Start

### Installation

```bash
pip install torch torchvision pillow
```

### Basic Usage

```python
import torch
import torchvision.transforms as transforms
from PIL import Image

# Load model
checkpoint = torch.load('best_model.pth', map_location='cpu')
model = ...  # Your model architecture
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

# Predict flower
def predict_flower(image_path):
    transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                             std=[0.229, 0.224, 0.225])
    ])
    
    image = Image.open(image_path).convert('RGB')
    image_tensor = transform(image).unsqueeze(0)
    
    with torch.no_grad():
        outputs = model(image_tensor)
        _, predicted = torch.max(outputs, 1)
    
    return predicted.item()

# Get flower name
flower_id = predict_flower('your_flower.jpg')
flower_name = class_names[flower_id]  # Use class_config.json
print(f"Predicted: {flower_name}")
```

### Model Info

- **What it does:** Identifies 102 different flower species
- **Input:** Flower images (224×224 pixels)
- **Output:** Flower name and confidence score
- **Architecture:** EfficientNet
- **Training:** 3 epochs on Oxford Flowers dataset

### Example Results

- 🌹 Input: rose_image.jpg → Output: "rose" (98.2%)
- 🌻 Input: sunflower.jpg → Output: "sunflower" (95.7%)  
- 🌷 Input: tulip.jpg → Output: "tulip" (92.3%)

### Files Included

- best_model.pth - Trained model weights
- class_config.json - Flower names mapping
- config.json - Model configuration
- labels.txt - List of all flower names

### Supported Flowers

102 species including:
- 🌹 Rose
- 🌻 Sunflower
- 🌷 Tulip
- 🌼 Daisy
- 💐 Lily
- 🏵️ Orchid
- 🌺 Hibiscus
- 🌸 Cherry Blossom
- And 94 more...

## For Developers

```python
# Get top-5 predictions
def top_k_predictions(image_path, k=5):
    # ... (implementation)
    return [
        {"flower": "rose", "confidence": 0.98},
        {"flower": "tulip", "confidence": 0.01},
        # ...
    ]
```

### License

MIT License - Free for personal and commercial use ✅

### Need Help?

- Model not loading? Check PyTorch version
- Wrong predictions? Use clear, centered flower images
- Other issues? Open a discussion on this repo

Download and start classifying flowers today! 🌸

Model by Naila Rais · Hosted on Hugging Face