title	emoji	colorFrom	colorTo	sdk	app_file	pinned
🌼 EfficientNetV2B0 Flower Classifier	🌸	yellow	pink	gradio	app.py	true

🌼 EfficientNetV2B0 Flower Classifier

An elegant and efficient image classifier trained to recognize 5 flower types: daisy, dandelion, roses, sunflowers, and tulips.
Powered by TensorFlow, fine-tuned using EfficientNetV2B0, and deployed with Gradio on Hugging Face Spaces.

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Live Demo

👉 Try the app here: Hugging Face Space

Upload a flower image and get the top 5 predictions with confidence scores.

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Model Details

• Backbone: EfficientNetV2B0 (keras.applications)
• Framework: TensorFlow 2.x
• Dataset: TensorFlow Flowers (~3,700 images, 5 classes)
• Classes: daisy, dandelion, roses, sunflowers, tulips
• Validation Accuracy: 91.28%
• Training Strategy:
  • Stage 1: 5 epochs (base frozen)
  • Stage 2: 5 epochs (fine-tuning all layers)
• Preprocessing: preprocess_input() scaled to [-1, 1]

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📓 Training Notebooks

✅ Kaggle: Flower Recognition – Fine-Tuning EfficientNetV2B0
Full training notebook with dataset loading, preprocessing, model building, and evaluation.

⚠️ Colab: (Archived) Training started in Google Colab but was moved to Kaggle due to GPU quota limitations.
You can still view the original Colab notebook here: Colab Fine-Tuning

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## 📁 Project Structure

efficientnet-flower-classifier/
├── app.py # Gradio app (entry point)
├── models/
│ └── flower_model.h5 # Trained Keras model
├── requirements.txt
└── README.md

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Run Locally

git clone https://github.com/YOUR_USERNAME/8_FlowerRecognition-HF.git  
cd 8_FlowerRecognition-HF  
pip install -r requirements.txt  
python app.py

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Dependencies

• tensorflow
• gradio
• numpy
• pillow

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Acknowledgments

• TensorFlow Flower Dataset

• EfficientNetV2 Paper — Tan & Le

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🧑‍💻 Author

Clay Mark Sarte
GitHub | LinkedIn