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README.md

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----
-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