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| title: 🌸 Flower Classifier | |
| emoji: 🚀 | |
| colorFrom: purple | |
| colorTo: purple | |
| sdk: streamlit | |
| app_file: src/app.py | |
| sdk_version: 1.54.0 | |
| pinned: false | |
| license: mit | |
| # 🌸 Flower Classification with EfficientNet | |
| Deep learning image classification project trained on the **104 Flower Species dataset** from Kaggle. | |
| ## 🚀 Overview | |
| - 🖼 104 Flower Classes | |
| - 🧠 EfficientNetB0 (Transfer Learning) | |
| - ⚙️ TensorFlow / Keras | |
| - 📦 TFRecord Dataset | |
| - 💾 Saved in modern `.keras` format | |
| --- | |
| ## 📊 Model Training | |
| The model was trained on Kaggle using GPU/TPU acceleration. | |
| Steps: | |
| 1. Load TFRecord dataset | |
| 2. Preprocess images (224x224) | |
| 3. Train EfficientNetB0 | |
| 4. Generate submission file | |
| --- | |
| ## 🌐 Streamlit Demo | |
| Run locally: | |
| ```bash | |
| pip install -r requirements.txt | |
| streamlit run app.py | |