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.

本文提出多阶段变形卷积神经网络用于目标检测，使用新的变形约束池化层来建模对象部分的变形，并提出多阶段训练策略和预训练策略来优化多个难度级别的分类器以及改变神经网络结构、训练策略来得到一组具有较大多样性的模型，在 ILSVRC 2014 中排名第二，显著提高了目标检测的平均精度。

DeepID-Net: 多阶段和可形变深度卷积神经网络用于目标检测