


\begin{table}[t]

\centering
\caption{Result of finetuning CLIP models with FLYP and LAM on iWildCam. Targeted DA is used here.}
    %\caption{iWildCam results for LAM and ensembling methods.
        %The best result is in \textbf{bold}.  Arrows indicate changes relative to {ERM+DA}. }
        \label{tab:flyp}
% and second best is \underline{underlined}. Arrows indicate changes relative to {ERM+DA}. The {\em Count $\downarrow$} represents the number of datasets on which the CR-based DG method is {worse} than ERM+DA: a {lower} count indicates better performance relative to ERM+DA.} %The rank is decided by summing the individual dataset rankings for each method and then ranking the total scores  (lower rank is better). }
%\vspace{-0.05cm}
\adjustbox{max width=\textwidth}{
\addtolength{\tabcolsep}{-1pt}
\begin{tabular}{llcc}
\toprule

%&&\multicolumn{2}{c}{{\textbf{iWildCam}}}  \\%& \textbf{{Count $\downarrow$}} \\
Model & Method & {{ID F1}} & {{OOD F1}} \\

\midrule

% &{ERM} &{{55.8}} &{{41.4}} \\%&{----}\\

%&{Model Soup} &{{57.6}}&{{43.3}}\\%&{----}\\
{CLIP-}&{FLYP} &{{56.9}}&{{43.4}}\\
 ViT-L/14&{FLYP+DA} &{\textbf{59.0}}&{{44.3}}\\
&{FLYP+DA+LAM} &{{\textbf{59.0}}}&{{\textbf{45.6}}}\\
\midrule

% {CLIP-} &{ERM}  &{{52.1}} &{{39.9}} 
% \\%&{----}\\
% {ViT-L/14@336}&{WiSE-FT} &{{55.8}}&{{46.4}}\\%&{----}\\
{CLIP-}&{FLYP} &{{59.9}}&{{46.0}}\\
{ViT-L/14@336}&{FLYP+DA} &{{58.9}}&{{47.1}}\\
&{FLYP+DA+LAM} &{{\textbf{60.9}}}&{{\textbf{48.7}}}\\



%\midrule
%\toprule
%{Pred. Diff.}&&&&&& &{N/A}\\

%\end{tabular}
%\label{tab:CR_comparison}
%
%%\end{center}
%%\vspace{-2mm}
%%\end{table*}
%%\begin{table*}[t]
%%\begin{center}
%%\caption{OOD performances of models trained with \textbf{RandAugment} using  ERM, ERM+DA, LAM and other CR-Based DG methods. The experimental setup mirrors that of Table \ref{tab:CR_comparison}, with the exception of using a different DA method.} 
%
%\
%
%
%\begin{tabular}{lcccccc}



%\midrule
%\toprule
%{Pred. Diff.}&&&&&& &{N/A}\\
\bottomrule
\end{tabular}}
% \vspace{-2mm}
\end{table}
