Label set
A label set is a collection of labels. Create a new label set if you want to upload model predictions and use features such as model-assisted labeling.
From the command line, install the Segments.ai Python SDK.
pip install --upgrade segments-ai
Initialize the Segments client using your API key.
from segments import SegmentsClient ...api_key = "" client = SegmentsClient(api_key)
Create a new label set with client.add_labelset(). You need to specify the name and (optionally) the description of the new label set.
client.add_labelset()
dataset_identifier = "panoptic/visible" name = "model-predictions" client.add_labelset(dataset_identifier, name)
For best performance, create a new dataset and choose a suitable data category.
Use client.upload_asset() to upload a local file to Segments.ai's asset storage service.
client.upload_asset()
filename = "shiba_inu_178.jpg" with open(f"path/to/{filename}", "rb") as f: asset = client.upload_asset(f, filename=filename) image_url = asset.url
Add a sample with client.add_sample(). For each sample, specify a name and the required sample attributes. If you do not have URLs for your assets, upload the assets to Segments.ai first.
client.add_sample()
dataset = "panoptic/visible" name = "shiba_inu_178" image_url = "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/jane/4f47973f-5568-47f1-8a7d-44bfeb6f0f76.jpg" attributes = {"image": {"url": image_url}} sample = client.add_sample(dataset, name, attributes)
Are you sure you want to delete this label set? All labels in the label set will be deleted as well.
Are you sure you want to delete 0 samples? The corresponding labels will be deleted as well.
