August 2, 2022
Xailient Detectum™ was created to fulfill the need for fast deep learning model inference at the Edge. Xailient with Movidius on Raspberry 3B+ gives you 70 fps with better software and an AI accelerator.
1Xailient outperforms the state-of-the-art MobileNet v2 model with and without the use of hardware accelerators.
2Xailient Detectum was created to fulfill the same real-time demands, making deep learning algorithms more efficient. The result is 4 times higher frame rate than the state-of-the-art MobileNet accelerated with the Movidius™ Neural Compute Stick.
We trained MobileNet v2 and Xailient Detectum using an open-source training dataset to create face detectors. These face detectors perform both localization and classification of faces in an image (i.e., generate bounding box data).
These models provided the baseline of state-of-the-art Edge-optimized AI, and their performance was measured in terms of inference speed in frames per second on Raspberry Pi 3B+
Four different experiments were run: MobileNet v2 Face Detector without Movidius, MobileNet v2 Face Detector with Movidius, Xailient Face Detector without Movidius, and Xailient Face Detector with Movidius.
The Baseline MobileNet v2 without Movidius NCS had an inference speed of 1 frame per second, and MobileNet v2 with Movidius NCS had an inference speed of 5 frames per second. At the same time, the Xailient model processed 20 frames per second without the Movidius NCS and processed 70 frames per second with Movidius.
March 14, 2022
Xailient specializes in extremely efficient low-power computer vision. Intel's OpenVINO specializes in maximizing the performance and speed of computer vision AI workloads. OpenVINO improved Xailient FPS 9.5x on Intel hardware to 448 FPS. Together, Xailient-Intel outperforms the comparable MobileNet_SSD by 80x. Even after Intel worked the OpenVINO magic on MobileNet_SSD, Xailient-OpenVINO is 14x faster.
November 29, 2021
Xailient’s Face Recognition enables high-speed edge AI processing with low-power consumption using Sony’s IMX500 – a chip so small it can fit on the tip of your finger.
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