TutorialsΒΆ
The following tutorials provide end-to-end guides on how to develop machine learning model using the YZLITE:
| Name | Description |
|---|---|
| Keyword Spotting - On/Off | Develop an ML model to detect the keywords: "on" or "off" |
| Keyword Spotting - Pac-Man | Develop a demo to play the game Pac-Man in a web browser using the keywords: "Left", "Right", "Up", "Down", "Stop", "Go" |
| Keyword Spotting - Alexa | Develop a demo to issue "Alexa" commands to the AVS cloud and locally play the response |
| Image Classification - Rock/Paper/Scissors | Develop an image classification ML model to detect the hand gestures: "rock", "paper", "scissors" |
| Model Training in the "Cloud" | Vastly improve model training times by training a model in the "cloud" using vast.ai |
| Logging to the Cloud | Log model files and metrics to the cloud during training and evaluation using Weights & Biases |
| Model Optimization for MVP Hardware Accelerator | Use the various YZLITE tools to optimize a model to fit within an embedded device's resource constraints |
| Keyword Spotting with Transfer Learning | Use a pre-trained model to quickly train a new model that detects the keywords: "one", "two", "three", "four" |
| Fingerprint Authentication | Use ML to generate unique signatures from images of fingerprints to authenticate users |
| ONNX to TF-Lite Model Conversion |
Describes how to convert an
ONNX formatted model file to the
.tflite model format required by embedded targets
|
| Model Debugging | Describes how to debug an ML model during training |
| Add an Existing Script to the YZLITE | Describes how to convert an existing Tensorflow training script to support the YZLITE training flow |
| Synthetic Audio Dataset Generation | Describes how to generate a custom audio dataset using synthetic data. This allows for training keyword spotting ML models with custom keywords |