This repository contains the implementation of a machine learning project aimed at classifying furniture images. The project uses a deep learning model for classifying images into various furniture categories. It is designed to be easily deployable using Docker, making it accessible for a wide range of users.
static/: Contains static files for the web interface.test/: Directory for test images or scripts.uploads/: Temporary storage for uploaded images for classification.Dockerfile: Instructions for building a Docker container for the project.app.py: The Flask application server for hosting the web interface.base.html&index.html: HTML templates for the web interface.furniture_test.ipynb: Jupyter notebook containing the code for training and testing the model.requirements.txt: Specifies the Python dependencies required by the project.
Ensure you have Docker installed on your system to run this project. If you prefer running it locally, ensure you have Python 3.x and the necessary libraries installed.
- Build the Docker image:
docker build -t furniture-classifier . - Run the container:
docker run -p 5000:5000 furniture-classifier
- Install Python dependencies:
pip install -r requirements.txt
- Start the Flask application:
python app.py
Navigate to http://localhost:5000 to access the web interface. Upload an image of furniture to classify it into categories such as chairs, tables, sofas, or beds.
The furniture_test.ipynb notebook contains the steps for training and evaluating the furniture classification model. Follow the instructions within the notebook to retrain the model or evaluate its performance on a new dataset.
Contributions to the Furniture Classifier project are welcome. Please read through the contributing guidelines before submitting pull requests or issues.
This project is licensed under the MIT License - see the LICENSE file for details.