labelbox-python | Labelbox Python Client | Data Labeling library
kandi X-RAY | labelbox-python Summary
kandi X-RAY | labelbox-python Summary
Labelbox is the enterprise-grade training data solution with fast AI enabled labeling tools, labeling automation, human workforce, data management, a powerful API for integration & SDK for extensibility. Visit Labelbox for more information. The Labelbox Python API offers a simple, user-friendly way to interact with the Labelbox back-end.
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Top functions reviewed by kandi - BETA
- Generate a set of images from the given labels
- Process a label
- Create a CocoImage from a label
- Gets the annotation lookup for the given annotations
- Creates a new data row and returns it
- Get a single resource from database
- Gets the current user
- Execute the query
- Create a new project
- Builds a relationship query
- Update a relationship
- Upload a document to a notebook
- Create a webhook
- Create a mask from a data row
- Adds labels to the given dataset
- Creates a mutation request
- Validates that all cached fields are referenced by this Entity
- Build an NDBase object from data
- Bulk deletes data row metadata
- Create a bulk import request
- Separate a coco annotation from a panoptic json file
- Creates a new DataRow from a dictionary
- Create a BulkImportRequest from a file
- Predict for a given image
- Generate a list of coco images from the given labels
- Create a bulk import request from a local file
labelbox-python Key Features
labelbox-python Examples and Code Snippets
user@machine:~$ export LABELBOX_API_KEY=""
user@machine:~$ python3
from labelbox import Client
client = Client()
# On prem
client = Client( endpoint = "")
# Local
client = Client(api_key=os.environ['LABELBOX_TEST_API_KEY_LOCAL'], endpoint="http://
user@machine:~$ export LABELBOX_TEST_API_KEY_LOCAL=""
make test-local # with an optional flag: PATH_TO_TEST=tests/integration/...etc LABELBOX_TEST_API_KEY_LOCAL=specify_here_or_export_me
user@machine:~$ export LABELBOX_TEST_API_KEY_STAGING=""
make
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python3 get-pip.py
export PATH=/Users//Library/Python/3.8/bin:$PATH
pip3 install -e .
pip3 install -r requirements.txt
pip install labelbox[data]
Community Discussions
Trending Discussions on Data Labeling
QUESTION
I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.
What I've done so far is make this representation with the same enumerator class.
A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?
...ANSWER
Answered 2021-Dec-30 at 13:57Instead of using Enum
you can use a dict
mapping. You can avoid loops if you flatten your dataframe:
QUESTION
I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.
In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space
i have this:
...ANSWER
Answered 2020-Nov-16 at 07:24Try something like:
QUESTION
Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.
Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.
Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.
...ANSWER
Answered 2020-Oct-27 at 22:39Is the data behind virtual network by any chance?
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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