Machine Learning
Example | Description |
---|---|
DEEPCRAFT Deploy Model Radar | This code example demonstrates how to deploy an DEEPCRAFT™ generated machine learning model. It comes pre-configured with a radar based model generated within DEEPCRAFT™ Studio. The code example collects radar data from the XENSIV™ 60 GHz radar sensor which is then sent to the machine learning model to detect specific gestures (push and circle). It uses the model.c/h files generated from within DEEPCRAFT™ Studio directly. New models based on the Gesture Detection project can be dropped into the project as-is. For more details, see the README on GitHub. |
DEEPCRAFT Deploy Ready Model | This code example demonstrates how to integrate a ready model library from DEEPCRAFT™ Studio on ModusToolbox™. The code example includes six different models where five model detect differet sounds such as baby-cry detection, cough detection, alarm detection, siren detection, and snoring detection which uses data from the pulse-density modulation (PDM) to pulse-code modulation (PCM) which is sent to the model for detection. The sixth model detects hand gestures which uses data from XENSIV™ radar sensor. For more details, see the README on GitHub. |
DEEPCRAFT Streaming Protocol | This ModusToolbox™ firmware project implements the DEEPCRAFT™ streaming protocol v2 for PSOC™ 6 MCU boards, allowing the streaming of sensor data and other information from the board into DEEPCRAFT™ Studio for development and testing of Edge AI models. For more details, see the README on GitHub. |
Dual CPU Cyberon | This code example shows how to use the dual CPU architecture of the PSOC™ 6 MCU to implement a keyword spotting and command processing application. For more details, see the README on GitHub. |
Machine Learning Gesture Classification | This code example demonstrates how to perform gesture classification based on motion sensor (accelerometer and gyroscope) data. The code example comes with a pre-trained model that classifies the following gestures: circle, square, and side-to-side. For more details, see the README on GitHub. |
Machine Learning Neural Network Profiler | This code example demonstrates how to run through the machine learning (ML) development flow with PSOC™ 6 MCU, where the end user has a pre-trained Neural Network (NN) model, which can be profiled and validated at the PC and target device. For more details, see the README on GitHub. |