The　proliferation　of　logo　has　driven　research　into　multiple　applications,　like　logo　duration　monitoring　in　advertising　videos　and　logo　infringement　detection.　Recently,　logo　research　has　attracted　a　rapt　attention　and　keen　interest　from　researchers.　In　these　studies,　logo　detection　is　challenging　due　to　the　characteristics　of　the　logo　(like　multi-scale,　large-scale　categories,　viewpoint　and　part　deformation　etc.)　and　the　complexity　of　its　background.　In　this　paper,　we　design　a　new　strong　baseline　method　based　on　an　adaptive　representation　algorithm,　called　obtaining　sufficient　features　for　logo　(OSF-Logo).　The　method　aims　to　address　the　challenges　of　the　multiscale　objects,　large-scale　categories,　viewpoint　logos　and　part　deformation　via　introducing　two　modules.　Specifically,　we　introduce　a　regulated　deformable　convolution　module　with　offsets　and　amplitudes　to　more　convolution　layers　in　the　stage　of　feature　extraction.　In　addition,　we　add　an　up-sampling　operator　to　FPN　for　aggregating　information　into　a　large　receptive　field.　The　experimental　results　on　several　publicly　available　datasets　demonstrate　the　effectiveness　of　OSF-Logo.