This project is a demo serverless application that performs object detection on uploaded images.
You can test the app with a click of a button by creating resources with Amazon Web Services. Check for the prerequisites in the following section.
Or you may edit the CloudFormation template to personalize the app as you wish and then launch it with AWS. Check out the template here.
The app is a single page website that accepts an input image for back-end serverless processing with AWS Lambda. It responds with the provided input image that has been marked with bouding boxes which represent detected objects. Each bounding box features a label that classifies the detected object.
The front-end was built on top on Create React App open-source project. Both the website and API are hosted on AWS.
The workflow starts with a user request that is handled by AWS Amplify powered by API Gateway. API Gateway is integrated with Lambda function that predicts coordinates of bounding boxes and corresponding labels. Due to considerable volume of machine learning libraries and model weights, all dependencies are stored on Amazon Elastic File System associated with the function.
To make predictions Lambda uses deep learning YOLOv2 model. It uses the inference code I wrote when working on one of the assignments of the Deep Learning Specialization course.
In order to launch the app you need to create resources with Amazon Web Services. For that you need an AWS account and optionally AWS CLI set up. Learn more here.
Download CloudFormation template
curl -O https://raw.githubusercontent.com/molly-moon/projects/master/app-object-detection/web-app/object-detection-app-stack.yml
To launch the stack via the console, click on the button below. Upload the template file in the Specify template section.
You can also launch the stack using AWS CLI. Note you need to substitute YOUR_PATH with your actual downloads folder path.
aws cloudformation create-stack --stack-name object-detection-app --template-body file://YOUR_PATH/object-detection-app-stack.yml
Stack creation time is about 30 min.
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Basic environment: VPC, public subnet, private subnet, Internet Gateway, NAT Gateway and all necessary attachments and associations
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S3 bucket for website hosting and for Lambda handler code storage
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Elastic File System for increased Lambda storage space
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Lambda function to execute machine learning inference code
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REST API with API Gateway to serve as a front-door to the application
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EC2 instance to perform automated configuration tasks:
- Download static files to S3 bucket for website hosting
- Mount Elastic File System
- Download Lambda handler code
- Download model weights and dependencies
The instance will self-terminate right after the setup is completed.
Almost all resources necessary to run the app are eligible for the AWS free tier. This excludes EC2 t2.large instance used for EFS configuration and NAT Gateway I used for secure Lambda and EFS access. For North Virginia region the instance price is $0.0928/hour and NAT Gateway $0.045/GB-hour. Considering we use the EC2 instance only for the initial setup that should not take more than 15 min, the overall cost of running the app should not exceed $0.14 for the first hour and $0.045 for each additional hour.
Don't forget to delete your resources:
- From the console: select the stack to be deleted and then click Delete button
- Using AWS CLI: run the command
aws cloudformation delete-stack --stack-name object-detection-app
When creating this demo I was inspired by an open-source project Serverless Stack by Anomaly Innovations. They created a step-by-step guide to help you build a full-stack serverless application hosted on AWS.
The inference code used to detect object uses YOLOv2, a deep learning model created by Joseph Redmon. The keras implementation of YOLOv2 I use in this project is one of my assignments from Deep Learning Specialization course created by deeplearning.ai. The course assignment was greatly inspired by Yet Another Darknet 2 Keras project by Allan Zelener.