Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ESOD:Edge-based Task Scheduling for Object Detection   Object Detection on the mobile system is a challenge in terms of everything.
Nowadays, many object detection models have been designed, and most of them
concentrate on precision. However, the computation burden of those models on
mobile systems is unacceptable. Researchers have designed some lightweight
networks for mobiles by sacrificing precision. We present a novel edge-based
task scheduling framework for object detection (termed as ESOD). In detail, we
train a DNN model (termed as pre-model) to predict which object detection model
to use for the coming task and offloads to which edge servers by physical
characteristics of the image task (e.g., brightness, saturation). The results
show that ESOD can reduce latency and energy consumption by an average of
22.13% and 29.60% and improve the mAP to 45.8(with 0.9 mAP better),
respectively, compared with the SOTA DETR model.

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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-lat
2
The categories of this paper are: math
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The categories of this paper are: gr-qc
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The categories of this paper are: supr-con
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The categories of this paper are: astro-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: atom-ph