76.6       79.4       **84.3**   75.5       **69.5**   56.9       83.7       84.0       **87.4**   57.9       81.3       **74.1**   84.1       85.3       **83.5**   77.8       49.2       76.7       **77.3**   **86.7**      77.2
  RON384++                    **77.6**   **86.0**   82.5       **76.9**   69.1       **59.2**   **86.2**   85.5       87.2       **59.9**   81.4       73.3       **85.9**   **86.8**   82.2       **79.6**   **52.4**   78.2       76.0       86.2        **78.0**

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![image](area_animals.pdf){width="0.9\linewidth"}

![image](area_vehicles.pdf){width="0.9\linewidth"}

![image](area_furniture.pdf){width="0.9\linewidth"}

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We optimize the models described above jointly with end-to-end training. After the matching step, most of the default boxes are negatives, especially when the number of default boxes is large. Here, we introduce a dynamic training strategy to scan the negative space. At training phase, each SGD mini-batch is constructed from $N$ images chosen uniformly from the dataset. At each mini-batch, (a) for objectness prior, all of the positive samples are selected for training. Negative samples are randomly selected from regions with negative labels such that the ratio between positive and negative samples is 1:3; (b) for detection, we first reduce the sample number according to objectness prior scores generated at this mini-batch (as discribed in Section \[combine\]). Then all of the positive samples are selected. We randomly select negative samples such that the ratio between positive and negative samples is 1:3. We note that recent works such as Faster 