Imagimob AI, the First tinyML Platform to Support Deep Learning Anomaly Detection
February 28, 2022
News
Imagimob announced that its new release of the tinyML platform, Imagimob AI, supports end-to-end development of deep learning anomaly detection.
According to the company, 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 time to market.
The anomaly detection solution from Imagimob has been tested and verified on real-world machine and sensor data.
What's new in the latest Imagimob AI release
New anomaly detection features
- 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 minutes
Other improvements
- Support for quantization of models in the graphical user interface. This includes quantized models, reducing 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 MCU's from Texas Instruments, Renesas, STMicroelectronics, Acconeer and Nordic Semiconductors
The new release is available. Sign up for a free trial today.
For more information, visit: https://www.mynewsdesk.com/imagimob/news/imagimob-ai-the-first-tinyml-platform-to-support-deep-learning-anomaly-detection-443070
Tiera Oliver is the assistant managing editor at Embedded Computing Design. She is responsible for web content editing, product news, and story development. She also manages, edits, and develops content for ECD podcasts, including Embedded Insiders.
She utilizes her expertise in journalism and content management to oversee editorial content, coordinate with editors, and ensure high-quality output across web, print, and multimedia platforms. She manages diverse projects, assists in the production of digital magazines, and hosts company podcasts by conducting in-depth interviews with industry leaders to deliver engaging and insightful discussions.
Tiera attended Northern Arizona University, where she received her bachelor's in journalism and political science. She was also a news reporter for the student-led newspaper, The Lumberjack.
