some confusion about classes in pretrained model on OpenImages about asl HOT 2 CLOSED

Hi mrT23
First of all, thank you very much for your fast and helpful answer.
The reason I was interested in the labels list is, that I want to apply some post-processing steps with NLP techniques to filter out the most distinct labels.
So if I understood you correctly, this means that, although some of the labels are non-sense, this has no implications as these indexes anyway refer to highly unlikely (because undertrained) labels. But in any case, it still holds that the network's output matches the index-to-class schema provided.
If that's the case this is perfectly fine for me. I totally agree that 5500 labels are more than sufficient and that the labels we get from running inference on your network are helpful.
I really appreciate your effort to provide a pretrained network on OI, given the problems with the dataset that you mentioned. To my knowledge, there are no other such networks available. So, from my side, we can close that issue and I'd reopen it if I found a discrepancy.

from asl.

Hi aurschmi.
The short answer - thanks for the review. i believe its fine.

The long answer:
Open Images is a complicated dataset. hard to download, hard to pre-process and hard to train.
for example , not all the download links are still alive. another example - many classes don't have in practice relevant pictures in the train set.

Out of the 9604 possible classes, i think the actual train (and test set) only contain about ~5500 valid classes, so the detector won't predict all classes. trust me, 5500 is plenty.

i recommend you to experiment with the detector on actual images. a good recipe for doing inference - for each image, choose the best ~20 labels that crossed a threshold of 0.95-0.99. you will see that it output good results. if you find a consistent discrepancy in the detector's output, let us know.

from asl.