Cifar10 Image Classifier using Transfer Learning

This is a convolutional neural network (CNN) project in Python that classifies images from the Cifar10 dataset. The model used is ResNet-50, which has been trained on the dataset and saved as "resnet.h5" file. A Streamlit app has been created to load the model and predict the class of an image uploaded by the user. Click here to visit app

Dataset

The Cifar10 dataset consists of 50,000 32x32 color images in 10 classes, with 5,000 images per class. The classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck. In this project, the images in dataset used have been resized to 124x124 for better accuracy.

Transfer Learning

Transfer Learning is a technique where a pretrained model is used for a project. Resnet-50 is a model trained on imagenet dataset (which contains over a Million images belonging to 1000 classes). In this project resnet-50 is redesigned to have 10 possible outputs corresponding to 10 classes and trained again on the given data.

Dependencies

To run the Streamlit app, you need to have the following dependencies installed:

Python 3.6+ tensorflow==2.4.1 keras==2.4.3 streamlit==0.80.0 Pillow==8.2.0

You can install them by running:

pip install -r requirements.txt

Usage

To run the app, use the following command:

streamlit run app.py

This will open the app in your web browser. You can then upload an image and click the "Predict" button to see the predicted class.

Files

• cifar_10_model_training.ipynb: Jupyter notebook used to train the ResNet-50 model on the Cifar10 dataset.
• resnet.h5: Saved model file.
• streamlit_app.py: Streamlit app code to load the model and predict the class of an image.
• requirements.txt: List of Python dependencies required for the app.

Credits

This project was completed as part of the Kaggle Cifar10 dataset competition.