Infoscribe.ai Blog

Data Labeling in Computer Vision: Ensuring Accurate Annotations.

By: Mathieu G.
2023-07-23
Data Labeling in Computer Vision: Ensuring Accurate Annotations.

Data labeling plays a pivotal role in the field of computer vision, enabling machines to understand and interpret visual data. As the demand for sophisticated AI systems and applications continues to grow, the importance of high-quality annotations becomes increasingly evident. A single mislabeled object or incorrect annotation can have profound consequences on the performance and reliability of computer vision models. Accurate data labeling is essential for training computer vision models to recognize and classify objects, detect patterns, and make informed decisions. It involves the process of annotating images or videos with precise labels that describe the objects, attributes, or regions of interest within the visual data. These annotations serve as ground truth references for training the models and evaluating their performance. The quality of data labeling directly impacts the performance and generalization ability of computer vision models. Inadequate or incorrect annotations can lead to significant errors, biases, and reduced overall accuracy. For example, mislabeled objects in autonomous driving applications can have severe safety implications, jeopardizing the lives of pedestrians and drivers. In medical imaging, misinterpretation due to inaccurate annotations can result in incorrect diagnoses and treatment plans. Ensuring high-quality annotations requires a combination of human expertise and advanced annotation tools. Human annotators with domain knowledge and expertise carefully review and label the visual data, taking into account various factors such as object boundaries, occlusions, and context. Additionally, using annotation tools that offer features like bounding boxes, polygons, and semantic segmentation masks can improve the accuracy and efficiency of the labeling process. The consequences of poor data labeling extend beyond the immediate impact on model performance. Inadequate annotations can lead to biased AI systems, perpetuating societal inequities and unfair outcomes. Biases in data labeling can result from human error, subjective interpretations, or the lack of diversity and representation in the annotated datasets. It is essential to address these challenges and strive for unbiased and inclusive data labeling practices to build robust and ethically sound computer vision models.

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Delivering accurate and consistent image annotation services

At Infoscribe, we understand that annotating images requires a great deal of time and utmost precision. Our primary goal is to ensure the highest level of quality to achieve 100% customer satisfaction.

To achieve this goal, we have implemented a rigorous training program for our annotators. Before being assigned to a project, each annotator is trained on best practices and is given test data to ensure a thorough understanding of the project and all possible scenarios. This allows us to deliver accurate and consistent results to our clients.

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Ensuring strict quality control: our process at infoscribe

At Infoscribe, we prioritize quality control to ensure accurate and consistent results for our clients. Here's how we do it:

1
Quality control
Before launching a project, we conduct a 100% quality control to analyze and address any frequent errors caused by misinterpretations or misunderstandings of instructions.
2
QC Reports
Our QC team creates a report for each quality control performed, listing and illustrating non-conformities detected with screenshots.
3
Corrections
Project managers use these QC reports to explain errors to annotators so they can make corrections.
4
Improvement
We also use whiteboards to communicate common errors and encourage continuous improvement of our quality.
5
Sampling inspection
Once the compliance rate is high and stable after a few weeks, we perform sampling inspection based on the ISO2859 standard (2000 version).
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Project management



Our project managers, who are in direct contact with our customers, comply with a detailed checklist designed to prevent mistakes and they report on a daily or weekly basis depending on the needs our customers expressed at the beginning of a project.