Object Detection using Yolo v3 #tinyml #cainvas #nocodeplatform #YOLOv3 #objectdetection

Did you know that YOLO v3 is capable of processing up to 60 frames per second on a single GPU? This remarkable speed makes it perfect for applications that require fast and accurate object detection. In this video, we will explore the benefits, challenges, and solutions associated with YOLO v3, as well as how TinyML and the cAInvas framework can help overcome these challenges.

cAInvas is built on no-code, low-code and full-code principles for AI applications and models derived from all popular platform like TensorFlow, keras, PyTorch and others.

cAInvas is bolstered by native integration of deepC inference framework and a compiler designed for embedded devices. It comes with python and c++ notebooks to develop end-to-end application with autoML and ensemble of ML models.

Best of all, it is free for all students, researchers and hobbyists to spread fast adoption of ML models for tiny devices. Reach out for more information:
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#tinyml #cainvas #lowcodeplatform #aitech #edge #nocode #nocodeplatform #recommended #CyberSecurity #MalwareDetection
#YOLOv3 #ObjectDetection #ComputerVision #DeepLearning #AI #MachineLearning #ConvolutionalNeuralNetworks #RealTimeDetection
#YOLO #YOLOv3Detection #YOLOv3Algorithm #YOLOv3Model #YOLOv3Network #YOLOv3Tech #YOLOv3Research #YOLOv3Tutorial
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