## 4 Experiments and Results
The mask prediction branch is trained with the resource label of GMD. As drawn in Figure 5, we analyze the embedding after global pooling in the mask branch. Embeddings of samples from GSD and embeddings of samples from GKD differ a lot and it is available to distinguish them. The mask predictions are all correct in the GKD and 99.89% of samples are classified correctly in the GSD. We combine this branch \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Input Size**} & \multirow{2}{*}{**Train Set**} & \begin{tabular}{c} **(Avg \(\pm\) Std)** \\ **Accuracy on GKD** \\ \end{tabular} & \begin{tabular}{c} **(Avg \(\pm\) Std)** \\ **Accuracy on GSD** \\ \end{tabular} & \begin{tabular}{c} **Highest Accuracy** \\ on GKD and GSD** \\ \end{tabular} \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} CNN baseline + global pooling \\ \end{tabular} } & \multirow{2}{*}{\(16\times 10\)} & Pretrain on & \multirow{2}{*}{99.49\%} & \multirow{2}{*}{86.09\%} & \multirow{2}{*}{-} \\  & and \(12\times 12\) & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} CNN baseline + global pooling \\ \end{tabular} } & \multirow{2}{*}{\(16\times 10\)} & Pretrain on & \multirow{2}{*}{99.49\%} & \multirow{2}{*}{94.20\% \(\pm\) 0.06\%} & \multirow{2}{*}{(99.49\%, 94.25\%)} \\  & and \(12\times 12\) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of multi fully-connected heads. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Input Size**} & \multirow{2}{*}{**Train Set**} & \begin{tabular}{c} **Avg \(\pm\) Std** \\ **Accuracy on GKD** \\ \end{tabular} & \begin{tabular}{c} **Avg \(\pm\) Std** \\ **Accuracy on GSD** \\ \end{tabular} & \begin{tabular}{c} **Highest Accuracy** \\ on GKD and GSD** \\ \end{tabular} \\ \hline \begin{tabular}{c} CNN baseline + global pooling \\ \end{tabular} & \multirow{2}{*}{\(16\times 12\)} & GKD & 99.36\% \(\pm\) 0.13\% & 69.56\% \(\pm\) 2.96\% & (99.23\%, 74.83\%) \\ \hline \begin{tabular}{c} CNN baseline + global pooling \\ \end{tabular} & \multirow{2}{*}{\(16\times 12\)} & GKD & 99.46\% \(\pm\) 0.08\% & 85.14\% \(\pm\) 2.78\% & (99.23\%, 89.89\%) \\ \hline \begin{tabular}{c} CNN baseline + global pooling \\ \end{tabular} & \multirow{2}{*}{\(16\times 12\)} & GSD & 69.13\% \(\pm\) 3.70\% & 95.12\% \(\pm\) 0.34\% & (76.21\%, 94.71\%) \\ \hline \begin{tabular}{c} CNN baseline + global pooling \\ \end{tabular} & \multirow{2}{*}{\(16\times 12\)} & GSD & 79.00\% \(\pm\) 3.35\% & 94.36\% \(\pm\) 0.34\% & (84.40\%, 93.91\%) \\ \hline \begin{tabular}{c} CNN baseline + global pooling \\ \end{tabular} & \multirow{2}{*}{\(16\times 12\)} & GMD & 99.03\% \(\pm\) 0.22\% & 92.85\% \(\pm\) 0.72\% & (99.23\%, 93.68\%) \\ \hline \
\begin{tabular}{c} CNN baseline + global pooling \\ \end{tabular} & \multirow{2}{*}{\(16\times 12\)} & GMD & 99.12\% \(\pm\) 0.21\% & 93.13\% \(\pm\) 0.59\% & (99.23\%, 93.91\%) \\ \hline
\begin{tabular}{c} CNN baseline + global pooling \\ \end{tabular} & \multirow{2}{*}{\(16\times 10\)} & \multirow{2}{*}{GMD} & \multirow{2}{*}{99.28\% \(\pm\) 0.19\%} & \multirow{2}{*}{93.41\% \(\pm\) 0.56\%} & \multirow{2}{*}{(99.49\%, 94.02\%)} \\  & and \(12\times 12\) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparisons of structures w/o global pooling. with the grasping stability prediction branch to have an overall prediction. Although the mask branch classifies a sample of GKD incorrectly, we find that the two heads in the grasping stability prediction branch predict similar in this sample. Therefore, as shown in Table 5, the overall accuracy does not decrease. In general, the whole network keeps the best checkpoint accuracy of (99.49%, 94.25%) on the test set of GDK and GSD. This result achieves a comparable level on both datasets with those separate models (with the result of (99.49%, 94.02%)) in Table 3, while this structure can lead to an end-to-end inference.