trellixvulnteam/image-classification_v0l1

basic CNN classifies images from CIFAR-10 dataset: ~65% accuracy - Udacity DLND project

Image-Classification

A Convolutional Neural Network (CNN) is trained to identify 10 different classes of images on the CIFAR-10 dataset. The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class.

Getting Started

Simply run the Jupyter Notebook dlnd_image_classification.ipynb or you can run the script image_classification.py

python image_classification.py

Prerequisites

You can install the required packages through Anaconda's environment manager using the machine-learning.yml file

conda env create -f machine-learning.yml

Then, activate the environment and run image_classification.py

activate machine-learning

Otherwise, check out the machine-learning.yml file for dependencies and their versions

Running the tests

Simply add test cases to problem_unittests.py or run it

python problem_unittests.py

Built With

• TensorFlow - The machine learning framework
• Anaconda - The environment manager
• Jupyter Notebook - The code documentation