Image Classifiers are used in the field of computer vision to identify the content of an image and it is used across a broad variety of industries, from advanced technologies like autonomous and augmented reality to eCommerce platforms. With that being said; in this project, I developed an image classification model that can automatically detect which kind of vehicle delivery drivers have, in order to route them to the correct loading bay and orders. The project focuses on assigning delivery professionals who have a bicycle to nearby orders and giving motorcyclists orders that are farther away.
The goal of the project is to ship a scalable and safe model that can tell bicycles apart from motorcycles.
The data was extracted from the Python version of the CIFAR-100 dataset. The CIFAR dataset is open source and generously hosted by the University of Toronto. In order to use the dataset, I transformed the data into a usable shape and format, split it into train and test then saved it into an AWS S3 bucket.
After EDA, I used an AWS build-in image classification algorithm to train the model and deploy it. The model performs at its best with an accuracy of over 80% on the test dataset.
The ML Workflow process
Lambda Function and Step Function
Invoke Lambda function
Successful Continous Integration and Continous Deployment using Step Function
├── Captured Data <- A folder that stores the captured data from the model predictions
├── images <- A folder that stores images
├── lambda functions <- A folder that stores the lambda functions
├── Scone_unlimited_notebook <- Documentation of the project in Jupyter Notebook
├── Stepfunction.json <- A JSON file that combines all the lambda functions after successful deployment
├── lambda.py <- A Python file to serialize data, classify, and filter inferences.
├── stop_instance.py <- A Python file that stops notebook instances after all deployment is done to save cost.
└── README.md <- Top-level README
Titilayo Amuwo