\begin{table*}[t]
    \caption{Results of classification of different self-supervised approaches with the supervised baseline on \acl{K19} and \acl{K16}. We present the results for different percentages of available data. The top results for the are highlighted in bold. We use weighted F1 score.}
    %\begin{center}
    \label{tbl:clspercentage}
    \centering
    \begin{footnotesize}
    \begin{threeparttable}
    \begin{tabular}{l c c c l c c c}
    \toprule
    \toprule
        & \multicolumn{3}{c}{\acl{K16}} & & \multicolumn{3}{c}{\acl{K19}} \\
        & \multicolumn{3}{c}{Labels fraction} & & \multicolumn{3}{c}{Labels fraction} \\
    \cmidrule{2-4} 
    \cmidrule{6-8}
    Methods & $10\%$ & $20\%$ & $50\%$ & & $1\%$ & $2\%$ & $5\%$\\
    \midrule
    Supervised$^{\ddagger}$ & 85.8\tnote{**} & 86.5\tnote{**} & 87.9\tnote{**} &  & \textbf{89.2}\tnote{+} & \textbf{89.9}\tnote{+} & \textbf{90.5}\tnote{+}\\
    SimCLR \cite{chen2020simple} & 79.6\tnote{**} & 78.9\tnote{**} & 78.6\tnote{**} & & 76.9\tnote{**} & 79.4\tnote{**} & 80.7\tnote{**}  \\
    SupContrast \cite{khosla2020supervised} & 60.8\tnote{**} & 73.2\tnote{**} & 80.8\tnote{**} & & 78.7\tnote{**} & 81.6\tnote{**} & 85.0\tnote{**}\\
    MoCoV2 \cite{chen2020improved} & \textbf{88.5} & \textbf{90.2} & \textbf{91.1} & & \textbf{89.9} & \textbf{90.3} & \textbf{90.6} \\
    \bottomrule
    \bottomrule
    \end{tabular}
    \begin{tablenotes}
        \item[$\ddagger$] Model initialized with ImageNet pre-trained weights.
        \item[+] $\ p\geq0.05$; $^{*}\ p<0.05$; $^{**}\ p<0.001$; unpaired t-test with respect to the top result.
    \end{tablenotes}
    \end{threeparttable}
    \end{footnotesize}
\end{table*}