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Our objective is to obtain a state-of-the art object category detector by employing a state-of-the-art image classifier to search for the object in all possible image sub-windows. We use multiple kernel learning of Varma and Ray (ICCV 2007) to learn an optimal combination of exponential χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> kernels, each of which captures a different feature channel. Our features include the distribution of edges, dense and sparse visual words, and feature descriptors at different levels of spatial organization. Such a powerful classifier cannot be tested on all image sub-windows in a reasonable amount of time. Thus we propose a novel three-stage classifier, which combines linear, quasi-linear, and non-linear kernel SVMs. We show that increasing the non-linearity of the kernels increases their discriminative power, at the cost of an increased computational complexity. Our contributions include (i) showing that a linear classifier can be evaluated with a complexity proportional to the number of sub-windows (independent of the sub-window area and descriptor dimension); (ii) a comparison of three efficient methods of proposing candidate regions (including the jumping window classifier of Chum and Zisserman (CVPR 2007) based on proposing windows from scale invariant features); and (Hi) introducing overlap-recall curves as a mean to compare and optimize the performance of the intermediate pipeline stages. The method is evaluated on the PASCAL Visual Object Detection Challenge, and exceeds the performances of previously published methods for most of the classes.