Amazon Web Services (AWS) has launched Deeplens, the “world’s first deep learning enabled video camera for developers”. Powered by an Intel Atom X5 processor with 8GB, and featuring a 4MP (1080p) camera, the fully programmable system runs Ubuntu 16.04, and is designed expand deep learning skills of developers, with Amazon providing tutorials, code, and pre-trained models.
AWS Deeplens specifications:
- Camera – 4MP (1080p) camera using MJPEG, H.264 encoding
- Video Output – micro HDMI port
- Audio – 3.5mm audio jack, and HDMI audio
- Connectivity – Dual band WiFi
- USB – 2x USB 2.0 ports
- Misc – Power button; camera, WiFi and power status LEDs; reset pinhole
- Power Supply – TBD
- Dimensions – 168 x 94 x 47 mm
- Weight – 296.5 grams
The camera can not only do inference, but also train deep learning models using Amazon infrastructure. Performance wise, the camera can infer 14 images/second on AlexNet, and 5 images/second on ResNet 50 for batch size of 1.
Six projects samples are currently available: object detection, hot dog not hot dog, cat and dog, activity detection, and face detection. Read that blog post to see how to get started.
But if you want to make your own project, a typical workflow would be as follows:
- Train a deep learning model using Amazon SageMaker
- Optimize the trained model to run on the AWS DeepLens edge device
- Develop an AWS Lambda function to load the model and use to run inference on the video stream
- Deploy the AWS Lambda function to the AWS DeepLens device using AWS Greengrass
- Wire the edge AWS Lambda function to the cloud to send commands and receive inference output
This steps are explained in details on Amazon blog.
Intel also published a press release explaining how they are involved in the project:
DeepLens uses Intel-optimized deep learning software tools and libraries (including the Intel Compute Library for Deep Neural Networks, Intel clDNN) to run real-time computer vision models directly on the device for reduced cost and real-time responsiveness.
Developers can start designing and creating AI and machine learning products in a matter of minutes using the preconfigured frameworks already on the device. Apache MXNet is supported today, and Tensorflow and Caffe2 will be supported in 2018’s first quarter.
Jean-Luc started CNX Software in 2010 as a part-time endeavor, before quitting his job as a software engineering manager, and starting to write daily news, and reviews full time later in 2011.