danystinson / yolov5-on-amazon-sagemaker Goto Github PK

Object detection allows us to identify and locate objects in images or videos. You may want to detect your company brand in pictures, find objects in an shelf, count the number of people in a shop and many other detection use cases which need to be fullfilled everyday. You only look once (YOLO) is a state-of-the-art, real-time object detection system presented in 2015. Nowadays YOLO has become a very popular algorithm to use when focusing on object detection.

Amazon SageMaker is a fully managed service to build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.

In this workshop you will learn how to use different Amazon Sagemaker features to train and deploy custom YOLOv5 models.

Here are the different sections you can find:

0. Label and prepare your dataset: Before we start creating a custom model, we need data, which has to be labelled and organized in the expected format. For this task we will make use of Amazon SageMaker Ground Truth, a feature that helps you build and manage your own data labeling workflows and data labeling workforce. Once you have labeled your dataset, you can choose to convert it to the expected format locally or using Amazon SageMaker Processing Jobs.

1. Train and test on Amazon SageMaker Studio: Once we have prepared the dataset we can train the custom YOLOv5 model. In this section you will download your dataset to train and test the model locally on Amazon SageMaker Studio.

2. Train and deploy with Amazon SageMaker: Training and testing locally is good to quickly test out your model, but for production you will probably want to train your models with more powerfull instances and deploy your model to an endpoint (having to manage the least infrastructure as possible). In this section you will learn how to make use of Amazon SageMaker Training Jobs and Amazon SageMaker Endpoints to train and deploy your custom model.

3. Train and deploy with Amazon SageMaker Pipelines: Once you have learned how to train and deploy your custom model with Amazon SageMaker Features, it is time to automate this process. For this task you are going to configure an Amazon SageMaker Pipeline, which will be in charge of model training, metrics evaluation and model creation and registration in the Amazon SageMaker Model Registry.