Epoch autocounting, kissme and precision about open-reid HOT 3 CLOSED

1. Why not using the epoch directly?
2. KISSME could be worse than Euclidean sometimes, especially with deep learning features. IMO CNN itself learns linear metric implicitly. So traditional metric learning might not be helpful in such case.
3. How did you use and split multiple datasets? Is test subset the same with single dataset training? If you simply mix the all the datasets together for evaluation, there will be much more gallery images, making the retrieval much more difficult.

from open-reid.

1. Because of diffeence in datasets. If I am training with viper or dukemtmc - 100 epochs is far not the same amount of iterations. And I cannot really compare a trained network in similar conditions. I never know in advance how many epochs I need, unless I check amount of ids and images for ids, and count amount of iterations... well... whatever, not a serious problem
3. I trained with one dataset, after with another one... and thus performed several iterations decreasing learning rate. I understand that merging datasets in advance would bring more benefits, but my idea was to check the fine-tuning ability, while using a model pre-trained with a different dataset

from open-reid.

1. One epoch is defined as one pass over the whole dataset, so it doesn't really apply here. You can just make a global iteration counter and use that.
2. You precision really depends on which dataset you use for the test. Unfortunately, all current datasets are not large enough to transfer to others properly.

from open-reid.