the proposed framework on a larger and more challenging dataset, we conduct experiments on MS COCO [@coco] and report results from test-dev2015 evaluation server. The evaluation metric of MS COCO dataset is different from PASCAL VOC. The average mAP over different IoU thresholds, from 0.5 to 0.95 (written as “0.5:0.95”) is the overall performance of methods. This places a significantly larger emphasis on localization compared to the PASCAL VOC metric which only requires IoU of 0.5. We use the 80k training images and 40k validation images [@fasterrcnn] to train our model, and validate the performance on the test-dev2015 dataset which contains 20k images. We use the $5$$\times10^{-4}$ learning rate for 400k iterations, then we decay it to $5$$\times 10^{-5}$ and continue training for another 150k iterations. As instances in MS COCO dataset are smaller and denser than those in PASCAL VOC dataset, the minimum scale $s_{min}$ of the referenced box size is 24 for 320$\times$320 model, and 32 for 384$\times$384 model. Other settings are the same as PASCAL VOC dataset.

  --------------------------- ------------- ---------- ---------- ----------
                                                                  
                                               0.5        0.75     0.5:0.95
  Fast R-CNN[@frcnn]              train        35.9        -         19.7
  OHEM[@ohem]                   trainval       42.5       22.2       22.6
  OHEM++[@ohem]                 trainval       45.9       26.1       25.5
  Faster R-CNN[@fasterrcnn]     trainval       42.7        -         21.9
  