## 5 Results and Discussion  
We employed ResNet-18 [44] components and a fully connected neural network [45] with random initialization for our classification task. Optimization was carried out using the cross-entropy loss function and the Adam optimizer [46], featuring a learning rate of 1 \(\times\) 10\({}^{-3}\), betas set to (0.9, 0.999), eps of 1 \(\times\) 10\({}^{-8}\) and weight decay of 0. Training was executed on a single NVIDIA A100 GPU with 40 GB of GPU memory [47] using a batch size of 64 to maximize GPU memory utilization. The model was trained for 250 epochs, with each epoch involving a full pass through all training images. Samples were dynamically determined as positive or negative with equal probabilities. The ground truth label from the MS COCO dataset was utilized to generate good or bad labels. To create a negative sample, a single error type and subtype were introduced to the ground truth label. There was a 50% probability of choosing either major Type A or major Type B for generating a negative sample. If major Type A was selected, there was a 33.3% chance that A1, A2 or A3 would be performed. If major Type B was chosen, there was a 50% chance that either B1 or B2 would be performed. These probabilities were designed to ensure that both major Types A and B as well as their subtypes had equal chances of being selected. Table 2 displays the performance metrics for the trained model [48] on the test set. The MS COCO training set serves as the test set for the machine learning-based label quality assurance model, with good and bad labels generated for testing and each having an equal probability of occurrence. The classification report on the test set indicates that the trained model can effectively differentiate between good-labeled and bad-labeled samples. A baseline random classifier, which provides random good or bad predictions for labeled samples, would have an accuracy of approximately 50%. In comparison, our classifier achieves an accuracy of 82%, correctly identifying the quality of labels for 82% of the samples. The support metric reveals that Class 0 (bad label) contains 54,473 samples, while Class 1 (good label) consists of 54,699 samples. Since there is an equal chance of a sample being assigned a good or bad label, these numbers are closely matched. The total number of test samples is 109,172. The classification report displays class-wise precision, recall, F1-score, macro and weighted averages, along with overall accuracy, precision and recall. Metrics demonstrate the predictive power of the machine learning-based quality assurance model. Figure 17 presents the training loss, validation loss and validation accuracy plots, with the epoch number on the \(x\)-axis and loss or accuracy on the \(y\)-axis. The decreasing trend in both training and validation losses, combined with the increasing validation accuracy, indicates that the neural network is effectively learning from the data and is able to distinguish between good and bad labels. \begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **F-Score** & **Support** \\ \hline
0 & 0.86 & 0.75 & 0.80 & 54,473 \\
1 & 0.78 & 0.88 & 0.83 & 54,699 \\ \hline accuracy & & & **0.82** & 109,172 \\ macro avg & 0.82 & 0.82 & 0.82 & 109,172 \\ weighted avg & 0.82 & 0.82 & 0.82 & 109,172 \\ \hline accuracy & 0.8166 & & & \\ precision & 0.8222 & & & \\ recall & 0.8166 & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Test classification report. We also examined the impact of neural network size on the classification accuracy by training smaller neural networks. This was accomplished by reducing the number of layers in the fully connected part of the neural network and removing the last layers within the ResNet-18 components. The analysis of our findings is presented below in Table 3. Our results indicate that increasing the size of the neural network may lead to further improvements in classification accuracy.