🌸 Flower Classification Model

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

Quick Start

Installation

pip install torch torchvision pillow

Basic Usage

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 (224Γ—224 pixels)
  • Output: Flower name and confidence score
  • Architecture: EfficientNet
  • Training: 3 epochs on Oxford Flowers dataset

Example Results

  • 🌹 Input: rose_image.jpg β†’ Output: "rose" (98.2%)
  • 🌻 Input: sunflower.jpg β†’ Output: "sunflower" (95.7%)
  • 🌷 Input: tulip.jpg β†’ 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
  • πŸ’ Lily
  • 🏡️ Orchid
  • 🌺 Hibiscus
  • 🌸 Cherry Blossom
  • And 94 more...

For Developers

# 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 βœ…

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 Β· Hosted on Hugging Face

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Dataset used to train nailarais1/image-classifier-efficientnet