Python SDK quickstart
On the Segments.ai web platform you can create datasets, upload samples, create releases and download labels. All of these - and more - can also be done programmatically with the Python SDK.
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On the Segments.ai web platform you can create datasets, upload samples, create releases and download labels. All of these - and more - can also be done programmatically with the Python SDK.
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First install the Segments.ai Python SDK using pip:
Import the necessary packages, and initialize the Segments client using your API key:
Let's create a new image segmentation dataset programmatically using . Note that this dataset will be created under the user account corresponding to the API key.
Now switch to the Segments.ai web platform and label the three images you just uploaded by pressing the "Start labeling" button.
When working with image segmentation datasets, you'll probably want to visualize the image and label at this point. The segments.utils
module offers some helper functions for that:
Now let's upload some images to this dataset using .
If the image file is on your local computer, you should first upload it to our asset storage service (using ) or to another cloud storage service.
We can verify that the dataset now contains 3 images using .
Once you've labeled some samples, you can programmatically retrieve their labels using .
The Python SDK offers many more functions besides the ones that were shown here. Have a look at the for the full list.
The Python SDK can also be used to upload labels into Segments.ai. This is particularly useful for setting up workflows, where you verify and correct model predictions instead of labeling from scratch.