Import
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As you already know, we have a concept of . Now, how can you import your dataset to Supervisely?
Dataset formats are very different (coco, cityscapes and others), and there are many more modalities (images, videos and others), and even just with images, there are many variations of file formats.
We donโt want you to convert anything yourself, so, to deal with that, here at Supervisely weโve put together a number of for every format out there (and if we donโt have one, you can write an or use the ).
Using Supervisely Apps or API, you can turn your images, videos and annotations into Supervisely projects and datasets: they will be stored in the and at any time you can them in this or another format.
Supervisely has three ways how to store your assets:
Store files locally
The default strategy is to place all uploaded and generated assets to the storage on the same server where Supervisely is installed, for example, on a hard drive.
Store files remotely
The instance administrator can configure the Supervisely platform to upload and store all generated assets to a remote cloud storage provider, such as S3. This can affect performance, since files need to be transferred over the network, but it is a more reliable method of data storage. This is only available on Enterprise Edition.
Store individual files remotely (โImport by Linkโ)
The hybrid approach that takes the best of both worlds. In this scenario, you don't store files locally (so that generated or uploaded manually files end up on a hard drive), but use or API methods to add images or videos to datasets โby linkโ. That means, that instead of uploading the actual content of the file, you provide a resource url (such as โhttps://โฆโ or โs3://โฆโ) and Supervisely will not store the content on the platform โ instead, it will load it from your remote provider on-fly. This method allows you to import huge datasets almost instantly, but you will have to manage the remote storage yourself.
Import using Web UI
The most simple and straightforward method of importing is uploading your data using one of our Supervisely Apps.
Import sample dataset
Save valuable time by starting with already prepared datasets. We provide access to a variety of ready-made data to speed up your start.
Import into an existing dataset
It is possible to add more assets such as images to the existing project or dataset.
Import using Team Files
you can just select the appropriate Supervisely App from the context menu of a folder in your Team Files โ and enjoy.
Import from Cloud
Want to contribute to Supervisely? Start with our GitHub page here.
Import using API & SDK
Save valuable time by starting with already prepared datasets.