## 3 Experiments
### Evaluation with Adversarial-Based Domain Adaptation  
To implement adversarial domain adaptation approach, we utilize the FlickrLogos-32 dataset [34] as source domain and Logos-32 plus dataset [35] as target domain. The training images of target domain (i.e., Logos-32 plus) are collected to represent a comprehensive real-world data presentation. These target domain images (Logos-32 plus dataset) are captured on different platforms and in different sizes, shape, illumination and viewpoints, whereas most of the trainnig images in the source domain (i.e., FlickrLogos-32) dataset are captured on plane and cylindrical surfaces and in selected viewpoints. The data distributions of these two datasets are very different from each other. The task of detection becomes very challenging when the model is trained on the source domain that has less comprehensive data representation and tested to the target domain that does not have the same distribution and style as source. To perform the experiment under the domain-shift problem, we needed a test set with different domain representations. We randomly selected 30 images for each class from the target domain (Logos-32 plus dataset) and created a new test set of 960 images, while the training set is source domain (FlickrLogos-32 dataset). The remaining images of target domain (Logos-32 plus dataset) are used during the training. Note that in this experiment only the source domain (FlickrLogos-32 dataset) is annotated, while the target domain (Logos-32 plus dataset) is not annotated. This is a case of domain shift (FlickrLogos-32 to Logos-32 plus, scene adaptation) because both datasets have training images with different data distribution and styles. The details of the datasets are provided in Table 7. The target domain has a total of 7830 images, of which 6870 are considered as training (unlabeled) and 960 images are used as test set. The training parameters setting is used as before. The initial learning rate for the discriminant network is 0.0001 which is decreased by \(\times\)0.1 at 90 and 120 epochs. The Adam optimizer is used. During training, the same batch-sized images from the source and target domains are used to train the model. In each epoch, only randomly selected 960 target images out of 6870 images have used for the trainnig. Table 8 shows the detection results of LogoNet: normal training, LogoNet: domain adaptation using class-wise heatmaps, and LogoNet: domain adaptation using Mid-level \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Detectors**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Lightweight** \\ **Methods** \\ \end{tabular} } & \multirow{2}{*}{**mAP**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Detection Time**} \\ \hline \multirow{3}{*}{CenterNet [14]} & No & 80.7 & 191.26 M & 0.1093 s \\  & CPD [33] & 77.9 & 72.42 M & 0.1145 s \\  & Propsoed Method & 79.0 & 27.94 M & 0.0833 s \\ \hline \multirow{3}{*}{LogoNet [15]} & No & 82.2 & 192.05 M & 0.1145 s \\  & CPD [33] & 78.8 & 73.19 M & 0.1073 s \\  & Propsoed Method & **79.7** & 28.73 M & 0.0885 s \\ \hline Dual-Attention & No & 82.5 & 192.05 M & 0.1166 s \\ LogoNet & CPD [33] & 78.9 & 73.20 M & 0.1145 s \\  & Proposed Method & 79.5 & 28.73 M & 0.0979 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance Evaluation of the Lightweight Methods on FlickrLogos-32 Dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{1}{c}{\begin{tabular}{c} **Labeled Training** \\ **Images** \\ \end{tabular} } & \multicolumn{1}{c}{\
\begin{tabular}{c} **Unlabeled** \\ **Target Images** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Test** \\ **Images** \\ \end{tabular} } \\ \hline FlickrLogos-32 & 960 & - & - \\ **(Source dataset)** & & & \\ Logos-32 plus & - & 6870 & 960 \\ **(Target dataset)** & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Detail of training data setting. feature maps (proposed approach). In our experiments, LogoNet trained in the normal setting achieves 63.2 mAP accuracy.