英文:
Will object detection/segmentation improve classification accuracy?
问题
如果我想进行分类任务，如果有边界框或像素级信息，分类得分会提高吗？
或者在分类方面，分类网络会胜过目标检测/分割网络吗？
英文:
if i want to do a classification task will the classification score improve if i have bounding box or pixel wise information?
or will a classification network outperform an object detection/Segmentation network in classification?
答案1
得分: 1
好问题。
答案如下：这取决于情况。
例如，假设您在10种狗上训练了YoloV3。然后，YoloV3的输出是一个带有相应类别的边界框。
然而，拥有两个神经网络，一个用于检测狗（目标检测），另一个用于分类（狗的种类）可能会得到更好的分数。
同样适用于语义/实例分割网络。
尽管没有固定的方法来确定哪种方法效果更好，但我个人倾向于认为第二步骤的流程可能会产生稍微更好的结果（这些观察是基于我的经验，因为我曾经在某个时候实施过这两种方法）。
英文:
Good question.
The answer is the following: it depends on the case.
For instance, say you train YoloV3 on 10 types of dogs. Then the output of YoloV3 is a bounding box with the corresponding class.
Having two neural networks however, one for detecting dogs(object detection) and the other one for classification(what type of dog) may perhaps yield a better score.
The same goes for semantic/instance segmentation network.
Although there is no recipe for saying which approach works better, I would personally incline to believe that the second step pipeline would yield slightly better results(there observations are based on my experience, since I have implemented both approaches at a point).
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