Object detection and classification in imagery using deep neural networks (DNNs) and convolutional neural networks (CNNs) is a well-studied area. For some applications, these AI approaches are considered to be reliable enough to use in production with minimal intervention. Popular methods include YOLO, SSD, Faster-RCNN, MobileNet, RetinaNet, and others. In most application contexts…
]]>This post is the second in a series (Part 1) that addresses the challenges of training an accurate deep learning model using a large public dataset and deploying the model on the edge for real-time inference using NVIDIA DeepStream. In the previous post, you learned how to train a RetinaNet network with a ResNet34 backbone for object detection. This included pulling a container…
]]>Some of the biggest challenges in deploying an AI-based application are the accuracy of the model and being able to extract insights in real time. There’s a trade-off between accuracy and inference throughput. Making the model more accurate makes the model larger which reduces the inference throughput. This post series addresses both challenges. In part 1, you train an accurate…
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