# Evaluation¶

## Image ReID¶

• Market1501, DukeMTMC-reID, CUHK03 (767/700 split) and MSMT17 have fixed split so keeping split_id=0 is fine.

• CUHK03 (classic split) has 20 fixed splits, so do split_id=0~19.

• VIPeR contains 632 identities each with 2 images under two camera views. Evaluation should be done for 10 random splits. Each split randomly divides 632 identities to 316 train ids (632 images) and the other 316 test ids (632 images). Note that, in each random split, there are two sub-splits, one using camera-A as query and camera-B as gallery while the other one using camera-B as query and camera-A as gallery. Thus, there are totally 20 splits generated with split_id starting from 0 to 19. Models can be trained on split_id=[0, 2, 4, 6, 8, 10, 12, 14, 16, 18] (because split_id=0 and split_id=1 share the same train set, and so on and so forth.). At test time, models trained on split_id=0 can be directly evaluated on split_id=1, models trained on split_id=2 can be directly evaluated on split_id=3, and so on and so forth.

• CUHK01 is similar to VIPeR in the split generation.

• GRID , iLIDS and PRID have 10 random splits, so evaluation should be done by varying split_id from 0 to 9.

• SenseReID has no training images and is used for evaluation only.

Note

The split_id argument is defined in ImageDataManager and VideoDataManager. Please refer to torchreid.data.

## Video ReID¶

• MARS and DukeMTMC-VideoReID have fixed single split so using split_id=0 is ok.

• iLIDS-VID and PRID2011 have 10 predefined splits so evaluation should be done by varying split_id from 0 to 9.