无监督模型 训练过程
Machine Learning, Artificial Intelligence, and Deep Learning are some of the most complex, yet highly demanded fields of expertise today. There are innumerable resources and tools to work in these fields, and one such popular tool is Supervisely.
机器学习，人工智能和深度学习是当今最复杂但要求很高的专业领域。 在这些领域中有无数的资源和工具可以使用，而Supervisely是其中一种流行的工具。
Supervisely is a web platform where we can build Deep Learning solutions. It is a service meant for dataset management, annotation, and preparation for Deep Learning. Supervisely is used by students, researchers, and businessmen to manage large-scale datasets and even preserve privacy by working with Supervisely on their servers.
Supervisely是一个Web平台，我们可以在其中构建深度学习解决方案。 它是用于数据集管理，注释和深度学习准备的服务。 学生，研究人员和商人使用Supervisely来管理大规模数据集，甚至通过在服务器上与Supervisely一起工作来保护隐私。
In this article, we will be looking at how to train models using Transfer Learning in Supervisely. To work with Supervisely, we first need to create an account. For non-commercial purposes, the account is created for free.
在本文中，我们将研究如何在Supervisely中使用Transfer Learning训练模型。 要与Supervisely合作，我们首先需要创建一个帐户。 出于非商业目的，免费创建该帐户。
When the account is first created, we are logged in as the admin user. There is a default workspace that is created. We can continue with this workspace or create our own. A sample of a custom workspace is shown below with the name MLOps_Task 6.
首次创建帐户时，我们以管理员用户身份登录。 存在一个默认的工作区。 我们可以继续使用此工作空间或创建自己的工作空间。 下面显示了名称为MLOps_Task 6的自定义工作区的示例。
Inside the new workspace, we can start a new project by importing the required dataset.
在新的工作空间内，我们可以通过导入所需的数据集来启动新项目。
The dataset can be imported by a simple drag-and-drop operation from the local system onto the given space.
可以通过简单的拖放操作将数据集从本地系统导入给定空间。
