Deep Learning Object Detection by Training & Deploying YOLOX
Deep Learning Object Detection by Training & Deploying YOLOX
Finetuning and testing a YOLOX model on custom built dataset. Creating and deploying object detection API to cloud New
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Created by : Neuralearn Dot AI
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.
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You will learn:
- Pre-deep learning object detection algorithms like Haarcascades
- Deep Learning algorithms like Convolutional neural networks, YOLO and YOLOX
- Object detection labeling formats like Pascal VOC.
- Creation of a custom dataset with Remo
- Conversion of our custom dataset to the Pascal VOC format.
- Finetuning and testing YOLOX model with custom dataset
- Conversion of finetuned model to Onnx format
- Experiment tracking with Wandb
- How APIs work and building your own API with Fastapi
- Deploying an API to the Cloud
- Load testing a deployed API with Locust
- Running 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.
