of cars by their model
Automatic scaling 1000+ of car pictures provides significant savings
Identification of types of
cars with near-human
European Emission Laws requires advertisers to label the car model in any photo shared on social media.
Our client would like a way to automate the labelling of car models when they are copied from personal accounts to the company account for advertising purposes.
A deep convolutional neural network was trained to identify car types.
It can identify roughly 200 different types of cars with near-human level accuracy (85%).
The client is able to automate the process of labelling cars, which frees up the company’s employees’ time for more valuable tasks.
Cars can be classified much faster than with a human labeler (a few milliseconds, compared to 1+ minutes for humans).
FRAMEWORK & TOOLS
The project was developed using Keras with a Tensorflow backend.
It was run as notebooks on Databricks, managed via Azure Machine Learning, and exposed for business process integration as a REST web service.
An inference program hosted on AWS Sagemaker could be queried with an image url, and would return the label and associated probability.