Step-by-step instructions on enabling DFU-Util can be found here: https://qeexo.com/stwin-now-supports-dfu-util/
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How do I enable DFU-Util to achieve flashing, data collection, and live testing through a single USB cable on my STWin device?
Here is a Python script (link here) that can convert a WAV file to CSV format so that your data becomes compatible with Qeexo AutoML. Depending on the original frequency of the sound file, the script conducts re-sampling to 16kHz, which is the ODR we work with.
If your sound files are in MP3 format, we recommend using ffmpeg to convert them to WAV files first before feeding them to this python script.
Please contact [email protected] if you have more questions on this.
Yes. Qeexo is continuing to add support for other hardware modules. Please stay tuned for our announcement for the next hardware support.
Qeexo AutoML currently supports IMU sensor, accelerometer sensor, magnetometer sensor, temperature sensor, altimeter/pressure sensor, humidity sensor, IR sensor, and microphone/audio sensor.
For Multi-class classification, Qeexo AutoML supports Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Gradient Boosting Machine (GBM), Logistic Regression (LR), Random Forest (RF), XGBoost (XGB) and Decision Tree (DT). For Single-class classification, Qeexo AutoML supports Local Outlier Factor (LOF) and Isolation Forest (IF). We are always adding support for more algorithms.
Custom hardware support is available for Enterprise tier users. For more information, please contact [email protected].
The architecture of CNN is configurable. Users have a choice of configuring several model parameters for CNN as well as ANN (feed-forward neural networks). Please refer to the Qeexo AutoML User Guide.
In order to make results statistically meaningful, there is a minimum amount of data needed for training a model. Minimum 10 training instances (length of time varies depending on sensor ODR for “Continuous” type data) or Events are required. If the data is insufficient, Qeexo AutoML will give recommendations on the additional amount necessary.
You can train up to 30 unique classes at once. With more classes, more data is recommended to ensure good performance.
AutoML performs automatic sensor selection, feature selection, and hyper-parameter optimization to maximize the performance of small datasets. However, if the data does not sufficiently represent the statistical diversity needed for building good machine learning models, more data is recommended. You can see if more data will help with accuracy through the learning curves generated for each model.
Data preprocessing is done automatically on Qeexo AutoML. You don’t need to write custom code.
Yes. Qeexo AutoML’s machine learning pipeline supports the tradeoffs between accuracy, model size, and latency. Soon we will release an option to expose these parameters to the end users so that users will be able to set, for example, the maximum allowed latency, in exchange for lower accuracy.
Camera-image classification is supported on Qeexo AutoML Vision, which is not yet available to the general public. If you have a specific use case, please contact us at [email protected].
Regression models are currently being developed and soon will be relased in Qeexo AutoML.
Everything is integrated in the Qeexo AutoML backend for the hardware platforms that we support, so there is no need for driver development. Camera image classification will be supported on Qeexo AutoML-Vision, available at a later time.
The anomaly detection model can be created on Qeexo AutoML as a Single-class classification project. Only data from the “normal” class is needed to train the classifier and anything not recognized within a threshold from the normal class is considered an anomaly.
When you select the target hardware, Qeexo AutoML becomes aware of its memory constraints, so that the models developed will always fit onto the device. We also use many different kinds of techniques such as feature selection, hyperparameter optimization, and engine setting optimization to make sure models are the smallest possible.
Yes. Qeexo provides custom hardware integration support for Enterprise tier users. For more information, please contact [email protected]
Qeexo AutoML has built-in data collection and labeling tools and can also accept data uploads via CSVs. Users can convert their data model to Qeexo-AutoML-supported CSV data format to upload and run it through the model-building pipeline. Please refer to the Qeexo AutoML User Guide for the CSV data format. Samples are also available in Downloads from within automl.qeexo.com.
Are you sending all of the sensor data to the cloud then downloading the trained machine learning models to the Edge?
Yes, for Bronze users on AWS, sensor data is sent to the cloud for model-building, and the trained models are downloaded onto the local device. For Enterprise tier users, there is an option to set up Qeexo AutoML on a local server.
Yes. All of the supported algorithms in Qeexo AutoML use regularization. Depending on the type of the algorithm, different regularization techniques are used.
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