Raspberry Pi Release
By taking advantage of this release, developers are able to leverage the DeepView QML development examples used to generate the demos captured in the example videos below and then build on them to explore their own custom Visual Intelligence ideas and solutions.
The DeepView on Pi release supports the rapid deployment of existing public models such as MobileNet to RPi 3 & RPi 4 platforms as well as enabling developers to deploy proprietary models for evaluation and test on the same devices. The streamlined model deployment methods offered by the Model Conversion Tools allows developers to focus on the creation of their Visual Intelligence solution without the need to build the entire embedded stack or pay a runtime size or performance penalty.
Register below and download the DeepView on Pi Release and access the Sample Projects and Tutorials.
Let us know what you think and have fun!
DeepView on Pi Demo Examples
The demo videos below were all created with the DeepViewML Toolkit using the DeepView on Pi Release. Each of these projects can be easily replicated by following the DeepView QML development examples and developers are then able to customize and extend these example projects to experiment with other applications and use cases using both public and proprietary ML models.
Single Shot Detection (SSD) Example
This video shows a standard TensorFlow SSD model converted and optimized to run on the DeepViewRT engine deployed to a Raspberry Pi 4 platform. In this example the model runs at 20fps.
With an appropriate dataset and the DeepViewML Toolkit, developers can retrain standard and custom SSD models to detect other objects of interest.
Real Time Segmentation Example
This video shows a standard TensorFlow Segmentation model converted and optimized to run on the DeepViewRT engine deployed to a Raspberry Pi 4.
Image segmentation annotates or 'paints' pixels in real time with a color denoting the classification of the specific object detected by the ML model.
In this example using a public model converted and optimized with the DeepView tools to run on the DeepView engine, the system is able to detect, classify and annotate humans and trains at 5 fps on a standard RPi 4 device.
Pose Estimation, Gesture Recognition and User Input
This video shows a modified PoseNet model converted and optimized to run on the DeepViewRT engine deployed to a Raspberry Pi 4 platform.
The user application has been extended to make use of the ML model output as an input to a gaming emulator deployed on the same target.
Developers are able to extend this example to provide gesture recognition and user input for a wide variety of useful applications (or just have fun playing other video games).