https://github.com/maruf-pfc/image_classifier_toolkit
A Django-based web platform that hosts multiple image classification models under one unified interface. Upload an image and get the predicted result instantly.
https://github.com/maruf-pfc/image_classifier_toolkit
animal-classification artificial-intelligence computer-vision deeplearning digit-classification digit-recognition django emotion-detection emotion-recognition flower-classification hacktoberfest image-classification keras-tensorflow machine-learning supervised-learning tensorflow vehicle-classification
Last synced: 2 days ago
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A Django-based web platform that hosts multiple image classification models under one unified interface. Upload an image and get the predicted result instantly.
- Host: GitHub
- URL: https://github.com/maruf-pfc/image_classifier_toolkit
- Owner: maruf-pfc
- License: mit
- Created: 2025-07-30T06:01:57.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-08-05T13:44:53.000Z (2 months ago)
- Last Synced: 2025-08-07T23:53:17.316Z (about 2 months ago)
- Topics: animal-classification, artificial-intelligence, computer-vision, deeplearning, digit-classification, digit-recognition, django, emotion-detection, emotion-recognition, flower-classification, hacktoberfest, image-classification, keras-tensorflow, machine-learning, supervised-learning, tensorflow, vehicle-classification
- Language: Python
- Homepage:
- Size: 10.5 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Image Classifier Toolkit
A Django-based web application with multiple image classification projects including flower, animal, fruit, emotion, vehicle, and digit classifiers. Each classifier has its own model and training pipeline using TensorFlow/Keras.
## Project Structure
```txt
image_classifier_toolkit/
├── common/ # Common utilities, e.g., training helper classes
├── config/ # Project configuration files (settings, urls, etc.)
├── datasets/ # Dataset folders for different classifiers
│ ├── flowers/
│ │ ├── daisy/
│ │ ├── dandelion/
│ │ ├── rose/
│ │ ├── sunflower/
│ │ └── tulip/
│ ├── animals/
│ ├── fruits/
│ ├── emotions/
│ ├── vehicles/
│ └── digits/
├── models/ # Saved trained model files (.h5)
│ ├── flower_model.h5
│ ├── animal_model.h5
│ ├── fruit_model.h5
│ ├── emotion_model.h5
│ ├── vehicle_model.h5
│ └── digit_model.h5
├── media/ # Uploaded media files (images for prediction)
├── flower_classifier/ # Django app for flower classification
├── animal_classifier/ # Django app for animal classification
├── fruit_classifier/ # Django app for fruit classification
├── emotion_classifier/ # Django app for emotion classification
├── vehicle_classifier/ # Django app for vehicle classification
├── digit_classifier/ # Django app for digit classification
├── manage.py # Django management script
└── README.md
```
## Dataset Downloads
You 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.
## Installation & Setup
1. **Clone the repository:**
```bash
git clone https://github.com/maruf-pfc/image_classifier_toolkit.git
cd image_classifier_toolkit
```
2. **Create and activate a Python virtual environment:**
```bash
python3 -m venv env
source env/bin/activate # Linux/macOS
# .\env\Scripts\activate # Windows
```
3. **Install dependencies:**
```bash
pip3 install -r requirements.txt
```
4. **Make sure you have TensorFlow installed and compatible with your environment.**
## Training Models
Each classifier has a custom management command to train its model.
Example for **Flower Classifier**:
```bash
python3 manage.py train_flower_model
```
Similarly, run commands for other classifiers, e.g.:
```bash
python3 manage.py train_animal_model
python3 manage.py train_fruit_model
python3 manage.py train_human_emotion_model
python3 manage.py train_vehicle_model
python3 manage.py train_digit_model
```
**Note:**
- Ensure the corresponding dataset is available inside `datasets//` folder before training.
- Models will be saved in the `models/` directory.
## Running the Project
Start the Django development server:
```bash
python3 manage.py runserver
```
Open your browser and visit: `http://127.0.0.1:8000/`
The home page lists all available classifiers with links to their prediction pages.
## Using the Web App
- On the classifier page (e.g., Flower Classifier), upload an image.
- The app will show the uploaded image, run prediction using the trained model, and display the predicted class with confidence.
- You can navigate back to home anytime.
## Flower Prediction Demo
## Animal Prediction Demo
## Notes
- Uploaded images are stored in the `media/` folder.
- Static files should be collected or served correctly in production.
- For large datasets or training on GPU, consider using Google Colab or a powerful machine.
- Keep your dataset folders organized to avoid `flow_from_directory` errors during training.
## Contributing
Contributions are welcome! Feel free to submit issues or pull requests.
## License
MIT License © [Md. Maruf Sarker](https://github.com/maruf-pfc)
## Contact
If you have questions or want to collaborate, reach out:
- Email: [mdmarufsarker.mms@gmail.com](mailto:mdmarufsarker.mms@gmail.com)
- GitHub: [https://github.com/maruf-pfc](https://github.com/maruf-pfc)
- Twitter: [@md_marufsarker](https://twitter.com/md_marufsarker)