  In this paper, we propose multi-stage and deformable deep convolutional
neural networks for object detection. This new deep learning object detection
diagram 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. With the
proposed multi-stage training strategy, multiple classifiers are jointly
optimized to process samples at different difficulty levels. 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 modeling averaging. The
proposed approach ranked \#2 in ILSVRC 2014. It improves the mean averaged
precision obtained by RCNN, which is the state-of-the-art of object detection,
from $31\%$ to $45\%$. Detailed component-wise analysis is also provided
through extensive experimental evaluation.
