Showing 4 open source projects for "ml"

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  • 1
    Label Studio

    Label Studio

    Label Studio is a multi-type data labeling and annotation tool

    ...Build custom UIs or use pre-built labeling templates. Detect objects on image, bboxes, polygons, circular, and keypoints supported. Partition image into multiple segments. Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models. ...
    Downloads: 23 This Week
    Last Update:
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  • 2
    Cleanlab

    Cleanlab

    The standard data-centric AI package for data quality and ML

    ...This package helps you find label issues and other data issues, so you can train reliable ML models. All features of cleanlab work with any dataset and any model. Yes, any model: PyTorch, Tensorflow, Keras, JAX, HuggingFace, OpenAI, XGBoost, scikit-learn, etc. If you use a sklearn-compatible classifier, all cleanlab methods work out-of-the-box.
    Downloads: 1 This Week
    Last Update:
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  • 3
    Toloka-Kit

    Toloka-Kit

    Toloka-Kit is a Python library for working with Toloka API

    Toloka-Kit is a Python library for working with Toloka API. The API allows you to build scalable and fully automated human-in-the-loop ML pipelines, and integrate them into your processes. The toolkit makes integration easier. You can use it with Jupyter Notebooks. Support for all common Toloka use cases: creating projects, adding pools, uploading tasks, and so on. Toloka entities are represented as Python classes. You can use them instead of accessing the API using JSON representations. ...
    Downloads: 3 This Week
    Last Update:
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  • 4
    Adala

    Adala

    Adala: Autonomous DAta (Labeling) Agent framework

    Adala is a data-centric AI framework focused on dataset curation, annotation, and validation. It helps AI teams manage high-quality training datasets by providing tools for data auditing, error detection, and quality assessment.
    Downloads: 0 This Week
    Last Update:
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