  Recent progress of deep learning has empowered various intelligent
transportation applications, especially in car-sharing platforms. While the
traditional operations of the car-sharing service highly relied on human
engagements in fleet management, modern car-sharing platforms let users upload
car images before and after their use to inspect the cars without a physical
visit. To automate the aforementioned inspection task, prior approaches
utilized deep neural networks. They commonly employed pre-training, a de-facto
technique to establish an effective model under the limited number of labeled
datasets. As candidate practitioners who deal with car images would presumably
get suffered from the lack of a labeled dataset, we analyzed a sophisticated
analogy into the effectiveness of pre-training is important. However, prior
studies primarily shed a little spotlight on the effectiveness of pre-training.
Motivated by the aforementioned lack of analysis, our study proposes a series
of analyses to unveil the effectiveness of various pre-training methods in
image recognition tasks at the car-sharing platform. We set two real-world
image recognition tasks in the car-sharing platform in a live service,
established them under the many-shot and few-shot problem settings, and
scrutinized which pre-training method accomplishes the most effective
performance in which setting. Furthermore, we analyzed how does the
pre-training and fine-tuning convey different knowledge to the neural networks
for a precise understanding.
