Imagimob New Platform for Deep Learning Anomaly Detection
The anomaly detection solution from Imagimob has been tested and verified on the real-world machine and sensor data.
Imagimob has rolled out tinyML platform, Imagimob AI, that supports end-to-end development of deep learning anomaly detection.
A big strength of deep learning anomaly detection is that it delivers high performance as well as eliminates the need for feature engineering, thus saving costs and reducing time-to-market.
Not only is deep learning anomaly detection better for eliminating the need for feature engineering but it can also leverage and deliver excellent performance on the new generation of powerful neural network processors that are now hitting the market. This means that when going to the edge customers can make the most of their hardware.
Feature engineering, in simple terms, is the act of converting raw observations into desired features using statistical or mathematical functions. Feature engineering normally requires domain expertise and is in general very time-consuming.
With the added support for autoencoder networks in Imagimob AI, developers can now build anomaly detection in less time, and with better performance.
Customers will be able to reduce development costs and shorten the time to market.
The anomaly detection solution from Imagimob has been tested and verified on the real-world machine and sensor data.
- End-to-end training and deployment of convolutional autoencoder networks for anomaly detection/predictive maintenance
- Anomaly detection starter-project for rotating machinery to get developers up and running in a minute
- Support for quantization of models in the graphical user interface. This includes quantized models, reducing the model size and decreasing inference time on MCUs without an FPU
- Improved model prediction – tracking of how models perform with millisecond resolution, before deploying given different confidence thresholds
- Faster training and model evaluation
- Increased support for large data sets
- Starter project for Renesas RA2L1 – Capacitive Touch Sensing Unit
- In total 8 starter projects, supporting sensors and MCUs from Texas Instruments, Renesas, STMicroelectronics, Acconeer and Nordic Semiconductors
