## 1 Introduction  
Modern artificial intelligence has come to rely heavily on artificial neural networks, which are predominantly trained through supervised learning with labeled data. A variety of neural network architectures have emerged to tackle diverse problems, and their performance is often dependent on the quality of the underlying labeled data. Large language models (LLMs), such as GPT-3 [1], have gained widespread popularity due to their impressive capabilities. The labeling tasks required for training models such as GPT-3 are relatively simple, facilitating easy scaling. These autoregressive models predict future words based on past words, and labels can be programmatically generated from vast text corpora. However, to harness more specific outputs from these models, fine-tuning of specially labeled data is necessary [2]. In contrast, generating inexpensive, high-quality labels for images is a more challenging process, even for pretraining large computer vision models. Labels, in the context of supervised learning, are the desired outputs or target values associated with the input data. They are used to guide the learning process of the neural network by providing a reference to the correct output. In object detection tasks, labels are the annotations that provide information about the location and category of objects within an image. For object detection, labels typically consist of two main components: bounding boxes and class labels. Bounding boxes are rectangular boxes drawn around each object of interest in the image. A bounding box is usually represented by the coordinates of its top-left and bottom-right corners, which define the position and size of the box. Class labels are the categories or classes assigned to each object within the bounding boxes. For example, if an object is a cat, its class label would be \"cat\". Another example of a task is to generate photo-realistic images from textual inputs, as demonstrated by models such as DALL-E 2 [3] and Stable Diffusion [4]. These models require datasets that contain both images and their corresponding textual descriptions. Labels play a crucial role in ensuring the quality of the generated images. Errors or inaccuracies in the textual descriptions can lead to the generation of images that do not align with the intended content. This research paper concentrates on the computer vision subfield within the broader context of modern artificial intelligence. Computer vision enables machines to extract meaning or semantics from images or videos, thereby allowing them to understand the contents. There are three popular tasks in computer vision: detection, recognition and segmentation. Three sample images from the Microsoft Common Objects in Context (MS COCO) dataset [5] are showcased, each demonstrating one of these tasks with a cat as the target object. Detection involves generating a bounding box around the object [6] (Figure 1), recognition involves assigning a tag to describe the object in the image [7] (Figure 2), and segmentation involves classifying pixels at the individual level [8] (Figure 3). Segmentation is the most comprehensive task, as detection and recognition can usually be derived from it. Figure 1: Detection: cat enclosed by a bounding box. In our research, we address problems related to data requirements for computer vision, with a particular focus on quality assurance of data labels. We propose a comprehensive framework for data collection, labeling and quality assurance of data and labels for machine learning projects. Furthermore, we provide a detailed explanation of our machine learning-based label quality assurance method to assess whether the ground truth assigned for a specific task and image is indeed accurate. This method allows us to determine if the labeled data accurately represent the ground truth, ensuring the reliability of the training data for machine learning models.