Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fine-tuning deep CNN models on specific MS COCO categories   Fine-tuning of a deep convolutional neural network (CNN) is often desired.
This paper provides an overview of our publicly available py-faster-rcnn-ft
software library that can be used to fine-tune the VGG_CNN_M_1024 model on
custom subsets of the Microsoft Common Objects in Context (MS COCO) dataset.
For example, we improved the procedure so that the user does not have to look
for suitable image files in the dataset by hand which can then be used in the
demo program. Our implementation randomly selects images that contain at least
one object of the categories on which the model is fine-tuned.

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The categories of this paper are: q-alg
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
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The categories of this paper are: econ
20
The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
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The categories of this paper are: eess
32
The categories of this paper are: atom-ph