We examine how the choice of data-side attributes for two important visual
tasks of image classification and object detection can aid in the choice or
design of lightweight convolutional neural networks. We show by experimentation
how four data attributes - number of classes, object color, image resolution,
and object scale affect neural network model size and efficiency. Intra- and
inter-class similarity metrics, based on metric learning, are defined to guide
the evaluation of these attributes toward achieving lightweight models.
Evaluations made using these metrics are shown to require 30x less computation
than running full inference tests. We provide, as an example, applying the
metrics and methods to choose a lightweight model for a robot path planning
application and achieve computation reduction of 66% and accuracy gain of 3.5%
over the pre-method model.

我们研究了在图像分类和目标检测的两个重要视觉任务中，选择数据方面属性如何帮助选择或设计轻量级卷积神经网络。通过实验，我们展示了四个数据属性 - 类别数目、目标颜色、图像分辨率和目标尺度如何影响神经网络模型的大小和效率。通过基于度量学习的内部和外部类别相似度度量，来指导这些属性的评估从而实现轻量级模型。使用这些度量进行评估，其计算量比完全进行推理测试少 30 倍。我们提供了一个应用这些评估和方法来选择一个轻量级模型用于机器人路径规划应用的示例，其计算量减少了 66%，精度提高了 3.5%。