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msic: Update README.md
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## Overview
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The OpenMV project aims at making machine vision more accessible to beginners by developing a user-friendly, open-source, low-cost machine vision platform. OpenMV cameras are programmable in Python3 and come with an extensive set of machine learning and image processing functions such as face detection, keypoints descriptors, color tracking, QR and Bar code decoding, AprilTags, GIF and MJPEG recording, and more.
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The OpenMV project brings machine vision to beginners with a user-friendly, open-source platform. OpenMV cameras are programmable in Python3 and feature advanced AI capabilities, including support for TensorFlow, ST Edge AI, and NPU acceleration through ARM Ethos-U55 and ST Neural-ART. The firmware includes a rich image image processing library, such as image filtering, feature detection, color tracking, QR and barcode decoding, AprilTag recognition, GIF and MJPEG recording and streaming, and much more.
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The OpenMV Cam comes with a cross-platform IDE (based on Qt Creator) designed specifically to support programmable cameras. The IDE allows viewing the camera's frame buffer, accessing sensor controls, uploading scripts to the camera via serial over USB (or WiFi/BLE if available) and includes a set of image processing tools to generate tags, thresholds, keypoints, and etc...
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<p align="center"><img width="320" src="https://raw.githubusercontent.com/openmv/openmv-media/master/boards/openmv-cam/v3/web-new-cam-v3-angle.jpg"></p>
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The latest generation of OpenMV cameras includes the N6 and AE3 models. The OpenMV-N6 is powered by the STM32N6 microcontroller, which features a 1GHz/600 GOPS NPU, hardware H.264 encoding, high-speed USB, ISP, GPU, and a high-speed μSD card slot. It also comes equipped with WiFi/BLE, Gigabit Ethernet, and extensive GPIO pins—combining the power of a single-board computer with the flexibility of a microcontroller.
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The first generation of OpenMV cameras is based on STM32 ARM Cortex-M Digital Signal Processors (DSPs) and OmniVision sensors. The boards have built-in RGB and IR LEDs, USB FS support for programming and video streaming, a uSD socket, and I/O headers breaking out PWM, UARTs, SPI, I2C, CAN, and more. Additionally, the OpenMV Cam supports extension modules (shields) using the I/O headers for adding a WiFi adapter, a LCD Display, a Thermal Vision Sensor, a Motor Driver, and more.
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The OpenMV project was successfully funded via Kickstarter back in 2015 and has come a long way since then. For more information, please visit [https://openmv.io](https://openmv.io)
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The OpenMV-AE3 is our smallest and most energy-efficient camera yet powered by the Alif Ensemble E3, with dual-core processors and dual Ethos-U55 NPUs. This ultra-low-power device can run YOLO models at 30 FPS while drawing only 0.25 W, and it consumes just 2.5 mW in deep sleep mode. It features a 1MP high-frame-rate color global shutter camera, an IMU, a microphone, an 8×8 distance sensor, and connectivity options such as high-speed USB, WiFi/BLE, and QWIIC—making it ideal for battery-powered AI vision applications.
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<p align="center"><img width="320" src="https://github.com/openmv/openmv-media/blob/2cef6b1eb4dff065e99f3cda0892aadabfd841cb/boards/openmv-cam/openmv-ae3/ae3-hero-crop.jpg"></p>
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Complementing the firmware is an intuitive, cross-platform IDE based on Qt Creator, specifically designed for machine vision development. The IDE lets users view the camera’s frame buffer in real time, adjust sensor settings, and run scripts directly on the device. It also provides a range of tools for image analysis, including tag generation, threshold definition, keypoint detection, and other processing functions.
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The OpenMV project was successfully funded on Kickstarter in 2015 and has evolved significantly since its inception. For more information, visit [https://openmv.io](https://openmv.io).
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## TensorFlow support
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The OpenMV firmware supports loading quantized TensorFlow Lite models. The firmware supports loading external models that reside on the filesystem to memory (on boards with SDRAM), and internal models (embedded into the firmware) in place. To load an external TensorFlow model from the filesystem from Python use [`tf`](https://docs.openmv.io/library/omv.tf.html) Python module. For information on embedding TensorFlow models into the firmware, and loading them, please see [TensorFlow Support](https://github.com/openmv/openmv/blob/master/lib/tflm/README.md).
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