Major updates: Model codes such as resnet.py and densenet.py keep the original style for easier modification. Generalize CrossEntropyLableSmooth to CrossEntropyLoss. --label-smooth should be called explicitly in order to add the label smoothing regularizer to the cross entropy loss. Add support to multi-dataset training. Datasets are specified by the arguments -s and -t, which refer to source datasets and target datasets, respectively. Both can take multiple strings delimited by space. For example, say you wanna train a model using Market1501+DukeMTMC-reID, just set -s market1501 dukemtmcreid. If you wanna test on multiple datasets, you can do -t market1501 dukemtmcreid cuhk03 msmt17. Arguments are unified in args.py. Dataloaders are wrapped into two classes, which are ImageDataManager and VideoDataManager (see data_manager.py). A datamanger is initialized by dm = ImageDataManager(use_gpu, **image_dataset_kwargs(args)) where image_dataset_kwargs() is implemented in args.py. Therefore, when new arguments are added to the data manager, you don't need to exhausively change everywhere in the code. What you need to update are (1) add new arguments in args.py and (2) update the input arguments in data_manager.py. BENCHMARK is replaced with MODEL_ZOO where model weights and training scripts can be downloaded.