  In this paper, we propose deformable deep convolutional neural networks for
generic object detection. This new deep learning object detection framework has
innovations in multiple aspects. In the proposed new deep architecture, a new
deformation constrained pooling (def-pooling) layer models the deformation of
object parts with geometric constraint and penalty. A new pre-training strategy
is proposed to learn feature representations more suitable for the object
detection task and with good generalization capability. By changing the net
structures, training strategies, adding and removing some key components in the
detection pipeline, a set of models with large diversity are obtained, which
significantly improves the effectiveness of model averaging. The proposed
approach improves the mean averaged precision obtained by RCNN
\cite{girshick2014rich}, which was the state-of-the-art, from 31\% to 50.3\% on
the ILSVRC2014 detection test set. It also outperforms the winner of
ILSVRC2014, GoogLeNet, by 6.1\%. Detailed component-wise analysis is also
provided through extensive experimental evaluation, which provide a global view
for people to understand the deep learning object detection pipeline.
