Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MegDet: A Large Mini-Batch Object Detector   The improvements in recent CNN-based object detection works, from R-CNN [11],
Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly
come from new network, new framework, or novel loss design. But mini-batch
size, a key factor in the training, has not been well studied. In this paper,
we propose a Large MiniBatch Object Detector (MegDet) to enable the training
with much larger mini-batch size than before (e.g. from 16 to 256), so that we
can effectively utilize multiple GPUs (up to 128 in our experiments) to
significantly shorten the training time. Technically, we suggest a learning
rate policy and Cross-GPU Batch Normalization, which together allow us to
successfully train a large mini-batch detector in much less time (e.g., from 33
hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone
of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st
place of Detection task.

6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph