First of all, thanks for your open-source codes.
Then, I want to make a request. Currently, the evaluate_movingfashion.py was custom made just for the SEAM-Match-RCNN model. For example, the temporal_aggregator and the way to compute distance are SEAM-Match-RCNN specified.
For models other than the SEAM-Match-RCNN, it is difficult to understand this evaluation code for evaluating performances on the MovingFashion dataset.
It would be appreciated if you could provide an evaluation code that works for all models, e.g., an evaluation function that takes as input the similarity matrix and labels of query-gallery samples, and outputs the top-k performances.
First of all, thanks for your open-source codes.
Then, I want to make a request. Currently, the evaluate_movingfashion.py was custom made just for the SEAM-Match-RCNN model. For example, the temporal_aggregator and the way to compute distance are SEAM-Match-RCNN specified.
For models other than the SEAM-Match-RCNN, it is difficult to understand this evaluation code for evaluating performances on the MovingFashion dataset.
It would be appreciated if you could provide an evaluation code that works for all models, e.g., an evaluation function that takes as input the similarity matrix and labels of query-gallery samples, and outputs the top-k performances.