How to understand the experiments in Table2?

[pILLOW-1]

A great work. I have a little problem understanding the experiments in Table2. My understanding is as follows:

1. For those unaligned pre-training methods, only use 1%, 5%, ..., 100% data chosen from COCO train2017 to finetune them. Is that correct?
2. For those aligned pre-training methods, first pre-train(box-domain) them on the whole COCO train2017, then use 1%, 5%, ..., 100% data from COCO train2017 to finetune them. Is that correct?
3. In addition to the above two questions, I notice that in section4.2, there is a statement: "We provide 5 different data folds for each low-data setting, and the final performance is the average of all results". How to understand the number '5' and 'low-data' here? A single result in Table2 is the average of 5 results??? I am confused about the logics here.

Appreciation for answering my questions!

[liming-ai]

Hi @pILLOW-1,

Thanks for your questions.

1. Yes. The 1%, 5%, ..., and 100% denote we choose the part of the data with accurate annotations to validate the effectiveness under the low-data settings.
2. Yes. Both the aligned methods and unaligned methods are the same settings.
3. The 'low data' here means the 1%, 5%, ..., 50% of whole COCO data. To avoid random error, for these subdatasets we run 5 experiments with different subset each, and then report the averaged results. All the results in Table 2 (except 100% data column) are calculated 5 times and calculate the average value.

Hope these explanations could help you.

[pILLOW-1]

Got it. Thanks!