Sure. Could you shed some more light on the large benchmark you mentioned? Something like state-of-the-art accuracy on the ImageNet-1k dataset? If so, I think you could use TensorFlow Model Garden to see what performance their implementations of image classification models get us to.
Similarly you will find implementations of other and most commonly used vision tasks – object detection and semantic segmentation. All have well established baselines on large datasets like MS-COCO, Places365, etc. For detection, mAP is the single most important metric we care about and for segmentation, IoU should do just fine.
As I am writing, I think the suite of vision APIs from TFX could also enable the following things:

Validator components to help developers understand the distribution of a large image dataset. I personally think this tooling is not available for enterprise-grade ML.
Utilities for running fast and distributed image similarity searches. There are already well established techniques for compressing embeddings and using them for fast retrieval. But having them integrated inside TFX would be a fun ride I think.

I  am starting as an MLE at a startup today, in fact. So expect more TFX related stuff from me in the coming days