This image classification app is a simple, user-friendly tool that allows users to upload an image and have it classified into one of the CIFAR-10 categories (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck). The app is built using Streamlit. Additionally, the repository contains a Jupyter Notebook with the code used to train the model, which uses TensorFlow to build a convolutional neural network.
Before running the app, you need to have Python installed on your system. If you don't have Python installed, you can download it from python.org.
To run this app, you need to install several Python libraries. The required libraries are listed below:
- Streamlit
- PIL (Python Imaging Library)
- NumPy
- TensorFlow
You can install these libraries using pip. Run the following command to install all required libraries at once:
pip install streamlit Pillow numpy tensorflow-
Clone the repository to your local machine:
git clone https://github.com/LouisChislett/Image_Classification_App.git
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Navigate to the cloned repository:
cd Image_Classification_App -
Run the app using Streamlit:
streamlit run app.py
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The app should now be running on your local server. Streamlit will provide a local URL which you can open in your web browser.
- Once the app is running, you will see an option to upload an image.
- Upload a
.jpg,.jpeg, or.pngimage file. - The app will display the uploaded image and predict its category among the CIFAR-10 classes.
The repository also includes a Jupyter Notebook that contains the code for training the image classification model. To run this notebook, you will need to install the following libraries:
- NumPy
- Matplotlib
- TensorFlow
- Seaborn
- Pandas
- Scikit-learn
These libraries can be installed via pip as follows:
pip install numpy matplotlib tensorflow seaborn pandas scikit-learnFor more information, you can contact me on LinkedIn at https://www.linkedin.com/in/louis-chislett-4ba82919b/