AWS DeepLens Sample Projects Overview

To get started with AWS DeepLens, use the sample project templates. AWS DeepLens sample projects are projects where the model is pre-trained so that all you have to do is create the project, import the model, deploy the project, and run the project. Other sections in this guide teach you to extend a sample project's functionality so that it performs a specified task in response to an event, and train a sample project to do something different than the original sample.

Artistic Style Transfer

This project transfers the style of an image, such as a painting, to an entire video sequence captured by AWS DeepLens.

This project shows how a Convolutional Neural Network (CNN) can apply the style of a painting to your surroundings as it's streamed with your AWS DeepLens device. The project uses a pretrained optimized model that is ready to be deployed to your AWS DeepLens device. After deploying it, you can watch the stylized video stream.

You can also use your own image. After fine tuning the model for the image, you can watch as the CNN applies the image's style to your video stream.

• Project model: deeplens-artistic-style-transfer

• Project function: deeplens-artistic-style-transfer

Object Recognition

This project shows you how a deep learning model can detect and recognize objects in a room.

The project uses the Single Shot MultiBox Detector (SSD) framework to detect objects with a pretrained resnet_50 network. The network has been trained on the Pascal VOC dataset and is capable of recognizing 20 different kinds of objects. The model takes the video stream from your AWS DeepLens device as input and labels the objects that it identifies. The project uses a pretrained optimized model that is ready to be deployed to your AWS DeepLens device. After deploying it, you can watch your AWS DeepLens model recognize objects around you.

Note

When deploying an SageMaker-trained SSD model, you must first run deploy.py (available from https://github.com/apache/incubator-mxnet/tree/v1.x/example/ssd/) to convert the model artifact into a deployable model. After cloning or downloading the MXNet repository, run the git reset --hard 73d88974f8bca1e68441606fb0787a2cd17eb364 command before calling deploy.py to convert the model, if the latest version does not work.

The model is able to recognize the following objects: airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, and TV monitor.

• Project model: deeplens-object-detection

• Project function: deeplens-object-detection

Face Detection and Recognition

With this project, you use a face detection model and your AWS DeepLens device to detect the faces of people in a room.

The model takes the video stream from your AWS DeepLens device as input and marks the images of faces that it detects. The project uses a pretrained optimized model that is ready to be deployed to your AWS DeepLens device.

• Project model: deeplens-face-detection

• Project function: deeplens-face-detection

Hot Dog Recognition

Inspired by a popular television show, this project classifies food as either a hot dog or not a hot dog.

It uses a model based on the SqueezeNet deep neural network. The model takes the video stream from your AWS DeepLens device as input, and labels images as a hot dog or not a hot dog. The project uses a pretrained, optimized model that is ready to be deployed to your AWS DeepLens device. After deploying the model, you can use the Live View feature to watch as the model recognizes hot dogs .

You can edit this model by creating Lambda functions that are triggered by the model's output. For example, if the model detects a hot dog, a Lambda function could send you an SMS message. To learn how to create this Lambda function, see Use SageMaker to Provision a Pre-trained Model for a Sample Project

Cat and Dog Recognition

This project shows how you can use deep learning to recognize a cat or a dog.

It is based on a convolutional neural network (CNN) architecture and uses a pretrained Resnet-152 topology to classify an image as a cat or a dog. The project uses a pretrained, optimized model that is ready to be deployed to your AWS DeepLens device. After deploying it, you can watch as AWS DeepLens uses the model to recognize your pets.

• Project model: deeplens-cat-and-dog-recognition

• Project function: deeplens-cat-and-dog-recognition

Action Recognition

This project recognizes more than 30 kinds of activities.

It uses the Apache MXNet framework to transfer learning from a SqueezeNet trained with ImageNet to a new task. The network has been tuned on a subset of the UCF101 dataset and is capable of recognizing more than 30 different activities. The model takes the video stream from your AWS DeepLens device as input and labels the actions that it identifies. The project uses a pretrained, optimized model that is ready to be deployed to your AWS DeepLens device.

After deploying the model, you can watch your AWS DeepLens use the model to recognize 37 different activities, such as applying makeup, applying lipstick, participating in archery, playing basketball, bench pressing, biking, playing billiards, blowing drying your hair, blowing out candles, bowling, brushing teeth, cutting things in the kitchen, playing a drum, getting a haircut, hammering, handstand walking, getting a head massage, horseback riding, hula hooping, juggling, jumping rope, doing jumping jacks, doing lunges, using nunchucks, playing a cello, playing a flute, playing a guitar, playing a piano, playing a sitar, playing a violin, doing pushups, shaving, skiing, typing, walking a dog, writing on a board, and playing with a yo-yo.

• Project model: deeplens-action-recognition

• Project function: deeplens-action-recognition

Head Pose Detection

This sample project uses a deep learning model generated with the TensorFlow framework to accurately detect the orientation of a person’s head.

This project uses the ResNet-50 network architecture to detect the orientation of the head. The network has been trained on the Prima HeadPose dataset, which comprises 2,790 images of the faces of 15 people, with variations of pan and tilt angles from -90 to +90 degrees. We categorized these head pose angles to 9 head pose classes: down right, right, up right, down, middle, up, down left, left, and up left.

To help you get started, we have provided a pretrained, optimized model ready to deploy to your AWS DeepLens device . After deploying the model, you can watch AWS DeepLens recognize various head poses.

• Project model: deeplens-head-pose-detection

• Project function: deeplens-head-pose-detection

Bird Classification

This project makes prediction of the top 5 bird species from a static bird photo captured by the AWS DeepLens camera.

This project uses the ResNet-18 neural network architecture to train the model with the CUB-200 dataset. The trained model can identify 200 different bird species. Because the number of categories are large, the project outputs only the top 5 most probable inference results. To reduce the background noise for improved precision, a cropped zone located at the middle of the camera image is used for inference. You can view the cropped zone from the project video streaming. By positioning the static bird photo in the box zone, inference results are illustrated on top left of the project view.

• Project model: deeplens-bird-detection

• Project function: deeplens-bird-detection