Camera: 2MP Sony IMX290 back-illuminated Starvis sensor, 1/2.8”, 12mm lens, 1920x1080 at up to 120fps, rolling shutter, wide dynamic range support.
IMU: TDK InvenSense ICM-20948 with 3-axis accelerometer, 3-axis gyro, 3-axis compass, SPI bus @ 7 MHz, can be synchronized with camera sensor.
HDMI 2.1 video + sound output, up to 4k @ 60 Hz.
MicroSD card slot, up to 104 MByte/s, for operating system, software and data.
2x USB 2.0 Type A ports (for keyboard, mouse, wifi, ethernet, etc).
1x mini-USB OTG port.
4-Pin UART (serial) port.
6-pin auxiliary power out for 5V, 3.3V, and 1.8V peripherals.
8-pin GPIO port (I2C + SPI, or 6x GPIO + GND + I/O voltage select).
Custom camera sensor connector, supports 1 or 2 sensors, 4x MIPI-CSI + IMU.
M.2 E-Key slot for 2230 PCIe x1/USB/SDIO/PCM/UART add-on cards (Coral TPU, WiFi, etc), supports custom JeVois extension for eMMC flash. (Note: PCIe x2 not supported).
Single 6-24 VDC input, 30 Watts max (including up to 15 Watts to power USB peripherals). Idle: 3 Watts. Running YOLOv2 on NPU: 5.3 Watts. Running CPU+NPU+TPU+VPU quad YOLO/SSD deep networks: 12 Watts.
OpenCV (latest) + OpenVino + all contribs and Python bindings preinstalled.
JeVois Core library with 30+ included machine vision modules preinstalled.
OpenGL ES 3.2, Vulkan 1.0, OpenCL 2.0, Coral Edge-TPU libraries.
Python 3.8 + numpy + scipy pre-installed.
Boost, Eigen, ImGui, glm, and many other C++ libraries pre-installed.
Install any extra aarch64 Ubuntu or Python packages using apt-get and pip3.
TensorFlow-Lite 2.5, Caffe, ONNX, MxNet, and Darknet deep learning support.
Import your own custom deep learning models to run inside JeVois-Pro.
Program your own machine vision pipelines in C++ or Python.
Full cross-compilation environment allows you to first develop and test your code on a standard Ubuntu Linux PC host computer, then cross-compile the same code for execution on the JeVois-Pro camera.
1.9.8
