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Quickly label training data and export to any format.
Streamline workflow by swiftly labeling training data and exporting it to any desired format, ensuring a seamless integration into your machine learning pipeline.
Our platform redefines the landscape of dataset labeling by leveraging the collective intelligence of a diverse crowd. Tasks distributed among a pool of skilled annotators from around the globe, ensuring rapid and accurate annotations. Whether you're working on image recognition, natural language processing, or any other machine learning task, our crowdsourcing dataset labeling solution provides the annotated data you need to train and validate models effectively.
Easily expand labeling operations by leveraging a skilled workforce, cutting-edge tools, and specialized expertise, ensuring the rapid and accurate annotation of large datasets.
Dataset annotation involves adding metadata, labels, or tags to raw data, usually in the form of images, to enhance its understanding and utility for machine learning models. This annotated data helps train and improve the accuracy of algorithms by providing labeled examples for the model to learn from. The annotations could include bounding box method, or any other relevant information depending on the specific goals of the machine learning task. Find out more about what dataset annotation meaning from the article.