You've probably come across the saying, "Data is the new oil," which holds immense truth in the world of AI/ML. However, it's important to remember that not all datasets are equal, as the quality of data directly impacts the outcomes of AI models. As the saying goes, "Garbage in, garbage out." When it comes to Computer Vision applications, having pristine training data is paramount to building a top-notch model. Interestingly, quality assurance (QA) consumes approximately 40% of the time dedicated to data annotation. This stage is crucial and cannot be skipped, as both AI and humans are susceptible to errors. Drawing from our 7+ years of expertise in AI training data, we have recognized that each annotation project possesses unique QA requirements. Therefore, we have meticulously refined these requirements and seamlessly integrated a highly accessible and robust QA module directly into your annotation workflow. The best part? It's fully configurable to meet your specific needs, and it's as cost effective as possible!
Real Time Data Labeling Quality Assurance: Nip Errors in the Bud!
We are all familiar with the concept of "garbage in, garbage out" in the realm of AI. The quality of data plays a critical role in determining the quality of an AI product, and that's why data annotation is so important. However, ensuring effective data annotation can be challenging due to the large volume of datasets, varying quality standards across projects, and tight time constraints. So, how can we address these challenges? One solution is to leverage AI to remember project quality standards for annotators and provide them with timely feedback. Ideally, a real-time feedback system should be in place, configured with all project quality standards before the annotation project begins. Our primary goal is to provide you with an exceptional and stress-free user experience while ensuring the highest quality in your annotated datasets and maintaining efficiency in the quality control process. Our real-time Data Labeling Quality Assurance (QA) module is highly configurable, offering a range of customizable options to meet your specific requirements. You can set specific quality thresholds, define validation criteria, and tailor the QA process according to the unique needs of your project. Based on discussions in our online community, we have developed a set of default quality rules that cover most common scenarios. However, we recognize that each project may have specific and unique QA criteria that go beyond the default rules. But fret not! In our Slack community, you can provide us with feedback and let us know your specific requirements. Our dedicated technicians will immediately update the rules needed for your project, ensuring that the QA module aligns perfectly with your quality standards. By offering a configurable QA module and actively engaging with our Slack community, we prioritize your needs and feedback. Our aim is to continuously improve and tailor our annotation platform to deliver exceptional results for your specific projects.
After the configuration, the real-time QA module would automatically measure the quality of the annotation based on the rules put into the system. The rules would be presented at three levels based on how severe it is: Information, Warning and Violation. A violation or several warnings fail the QA test and prevent annotators from submission; the recommended information tag is AI's effort trying to make the annotation more precise. If the annotation is not passing the QA system, you will see a red sign on the QA button on the top right corner of the interface, a signal showing which annotation fails the test, and a report document showing how the annotation fails the QA test. The real-time QA section allows you to thoroughly track the quality of the annotation from the beginning to the end; it improves the quality consistency of the annotation and boosts both the AI and manual annotation efficiency.
Batch Quality Assurance: Evolution within Annotation
In addition to our real-time QA module, we offer an innovative batch QA feature that allows you to perform comprehensive quality checks on your annotated data and identify any annotation violations across the entire dataset. Once you have completed annotating a dataset, you can initiate a batch quality assurance process by creating a QA task. This initiates a Batch QA Job that provides you with a data score and a detailed data quality report encompassing both the annotation quality and the overall dataset quality.
After creating the Batch QA Job, you end up with a data score and a comprehensive data quality report regarding the annotation and the whole dataset. The comprehensive data quality report presents a clear and detailed overview of the performance of your data annotation. It enables you to easily identify any areas that require improvement or correction within your dataset. This report provides valuable insights, allowing you to thoroughly examine your entire dataset and address any errors or inconsistencies that may have occurred during the annotation process. By utilizing the batch QA feature, you can efficiently review and enhance the quality of your dataset. The clear presentation of the report empowers you to identify specific areas that need attention and take corrective measures accordingly. This ensures that your dataset meets your project-specific standards of quality and accuracy.
Overall, the batch QA section complements our real-time QA module, providing you with an additional tool to conduct group checks and assess the overall quality of your annotated data. By leveraging this feature, you can thoroughly evaluate and improve your entire dataset, ensuring that your annotations are reliable and of the highest quality.
