## 4 Results and Discussion  
In this section, we present the results corresponding to the best-performing configurations obtained after a comprehensive evaluation of all tested model architectures and hyperparameter settings. Initially, a classification task was undertaken, categorizing accessories into four distinct classes: necklaces, rings, earrings, and bracelets. After thorough analysis to determine the optimal configuration for each considered encoder and decoder combination, the most favorable outcomes in terms of test Correct Classification Rate (CCR) were attained using VGG-16 as the CNN and GRU as the RNN, as detailed in Table 2. This table also includes additional metrics, such as validation CCR and Loss. Following the selection of the best configuration, a more comprehensive analysis was conducted, incorporating additional metrics like precision, recall, and F1-Score to assess the model's performance in classifying each jewelry class independently. Precision measures the accuracy of positive predictions, recall assesses the model's ability to capture all positive instances, and F1-Score provides a balance between precision and recall, offering a comprehensive evaluation of the model's performance. Table 3 illustrates the model's effectiveness in the classification task, achieving scores exceeding 91% for all metrics and accessory types, with the exception of bracelets, which proved more challenging to detect. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CNN** & **RNN** & **Neurons** & **Val. CCR** & **Val. Loss** & **Test CCR** \\ \hline InceptionV3 & GRU & 1024 & 0.9187 & 0.1026 & 0.9161 \\
**VGG-16** & **GRU** & **512** & **0.9314** & **0.0390** & **0.9464** \\ MobileNet & GRU & 64 & 0.8851 & 0.2696 & 0.8802 \\ InceptionV3 & LSTM & 1024 & 0.9487 & 0.0301 & 0.9193 \\ VGG-16 & LSTM & 512 & 0.9597 & 0.0542 & 0.9227 \\ MobileNet & LSTM & 64 & 0.8811 & 0.2208 & 0.8775 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Best model for identification of the class: necklace, ring, earring, and bracelet. It is worth noting that an optimization of the various parameters under consideration was conducted, yielding the best results with the following configuration: number of neurons = 512; batch size = 8; optimizer = Adam; and learning rate = 0.001. In the context of the primary task of generating comprehensive captions from images of accessories, optimal results were achieved with the VGG-16 and GRU combination. MobileNet was excluded from this investigation due to its tendency to frequently get stuck in local optima, leading to suboptimal performance in handling the specific challenges posed by image captioning in the context of jewelry. Table 4 presents a comparison of all considered encoder-decoder combinations for this task. In this experimentation, an optimization of the different parameters considered was carried out, resulting in the best outcomes with the following configuration: number of neurons = 256; batch size = 16; optimizer = [PERSON]; and learning rate = 0.001. Furthermore, the assessment of our proposed approach revealed that the top-performing model exhibited excellent captioning accuracy, closely aligning with the original labels from online jewelry stores. Notably, the captioning accuracy for rings, necklaces, and earrings is very promising. However, the classification accuracy for bracelets, while still commendable, was slightly lower in the test samples. The evaluation criteria involved a meticulous comparison of whether the generated captions precisely matched the authentic descriptions of the accessories captured in the images. It is important to acknowledge certain limitations in our model. The observed misclassifications, particularly in the case of bracelets, can be attributed to the inherent challenges posed by accessories with similar shapes and materials. Additionally, instances arose where the same type of accessory could exhibit variations in materials or jewels, further contributing to the nuanced nature of accurate classification.