Description
Object detection algorithms are everywhere. With creation of much more efficient models from the early 2010s, these algorithms which now are built using deep learning models are achieving unprecedented performances. In this course, we shall take you through an amazing journey in which you'll master different concepts with a step by step approach. We shall start from understanding how object detection algorithms work, to deploying them to the cloud, while observing best practices. You will learn: Pre-deep learning object detection algorithms like HaarcascadesDeep Learning algorithms like Convolutional neural networks, YOLO and YOLOXObject detection labeling formats like Pascal VOC. Creation of a custom dataset with RemoConversion of our custom dataset to the Pascal VOC format. Finetuning and testing YOLOX model with custom datasetConversion of finetuned model to Onnx formatExperiment tracking with WandbHow APIs work and building your own API with FastapiDeploying an API to the CloudLoad testing a deployed API with LocustRunning object detection model in c++If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals! This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum, will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time. Enjoy!!!
