{"id":30057147,"url":"https://github.com/maruf-pfc/image_classifier_toolkit","last_synced_at":"2026-04-28T16:01:42.564Z","repository":{"id":307371317,"uuid":"1028829131","full_name":"maruf-pfc/image_classifier_toolkit","owner":"maruf-pfc","description":"A Django-based web platform that hosts multiple image classification models under one unified interface. 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Each classifier has its own model and training pipeline using TensorFlow/Keras.\n\n## Project Structure\n\n```txt\nimage_classifier_toolkit/\n├── common/                    # Common utilities, e.g., training helper classes\n├── config/                    # Project configuration files (settings, urls, etc.)\n├── datasets/                  # Dataset folders for different classifiers\n│ ├── flowers/\n│ │ ├── daisy/\n│ │ ├── dandelion/\n│ │ ├── rose/\n│ │ ├── sunflower/\n│ │ └── tulip/\n│ ├── animals/\n│ ├── fruits/\n│ ├── emotions/\n│ ├── vehicles/\n│ └── digits/\n├── models/                    # Saved trained model files (.h5)\n│ ├── flower_model.h5\n│ ├── animal_model.h5\n│ ├── fruit_model.h5\n│ ├── emotion_model.h5\n│ ├── vehicle_model.h5\n│ └── digit_model.h5\n├── media/                     # Uploaded media files (images for prediction)\n├── flower_classifier/         # Django app for flower classification\n├── animal_classifier/         # Django app for animal classification\n├── fruit_classifier/          # Django app for fruit classification\n├── emotion_classifier/        # Django app for emotion classification\n├── vehicle_classifier/        # Django app for vehicle classification\n├── digit_classifier/          # Django app for digit classification\n├── manage.py                  # Django management script\n└── README.md\n```\n\n## Dataset Downloads\n\nYou need to [download](https://drive.google.com/drive/folders/157V2o8Y8Mb4h74YgcWNvLV490ZZpxmJ1?usp=drive_link) datasets for each classifier and place them inside the `datasets` folder as shown above.\n\n## Installation \u0026 Setup\n\n1. **Clone the repository:**\n\n   ```bash\n   git clone https://github.com/maruf-pfc/image_classifier_toolkit.git\n   cd image_classifier_toolkit\n   ```\n\n2. **Create and activate a Python virtual environment:**\n\n   ```bash\n   python3 -m venv env\n   source env/bin/activate   # Linux/macOS\n   # .\\env\\Scripts\\activate  # Windows\n   ```\n\n3. **Install dependencies:**\n\n   ```bash\n   pip3 install -r requirements.txt\n   ```\n\n4. **Make sure you have TensorFlow installed and compatible with your environment.**\n\n## Training Models\n\nEach classifier has a custom management command to train its model.\n\nExample for **Flower Classifier**:\n\n```bash\npython3 manage.py train_flower_model\n```\n\nSimilarly, run commands for other classifiers, e.g.:\n\n```bash\npython3 manage.py train_animal_model\npython3 manage.py train_fruit_model\npython3 manage.py train_human_emotion_model\npython3 manage.py train_vehicle_model\npython3 manage.py train_digit_model\n```\n\n**Note:**\n\n- Ensure the corresponding dataset is available inside `datasets/\u003cclassifier_name\u003e/` folder before training.\n- Models will be saved in the `models/` directory.\n\n## Running the Project\n\nStart the Django development server:\n\n```bash\npython3 manage.py runserver\n```\n\nOpen your browser and visit: `http://127.0.0.1:8000/`\n\nThe home page lists all available classifiers with links to their prediction pages.\n\n## Using the Web App\n\n- On the classifier page (e.g., Flower Classifier), upload an image.\n- The app will show the uploaded image, run prediction using the trained model, and display the predicted class with confidence.\n- You can navigate back to home anytime.\n\n## Flower Prediction Demo\n\n![Predict Flower](./images/PredictFlower.png)\n\n## Animal Prediction Demo\n\n![Predict Animal](./images/PredictAnimal.png)\n\n## Notes\n\n- Uploaded images are stored in the `media/` folder.\n- Static files should be collected or served correctly in production.\n- For large datasets or training on GPU, consider using Google Colab or a powerful machine.\n- Keep your dataset folders organized to avoid `flow_from_directory` errors during training.\n\n## Contributing\n\nContributions are welcome! Feel free to submit issues or pull requests.\n\n## License\n\nMIT License © [Md. Maruf Sarker](https://github.com/maruf-pfc)\n\n## Contact\n\nIf you have questions or want to collaborate, reach out:\n\n- Email: [mdmarufsarker.mms@gmail.com](mailto:mdmarufsarker.mms@gmail.com)\n- GitHub: [https://github.com/maruf-pfc](https://github.com/maruf-pfc)\n- Twitter: [@md_marufsarker](https://twitter.com/md_marufsarker)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaruf-pfc%2Fimage_classifier_toolkit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmaruf-pfc%2Fimage_classifier_toolkit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaruf-pfc%2Fimage_classifier_toolkit/lists"}