Pretrained resnet 34 is used. Fully connected layer with 512 neurons are added to the end of the net.
--dataroot DATAROOT path to dataset
--batch_size BATCH_SIZE
batch size for train, default=128
--batch_size_test BATCH_SIZE_TEST
batch size for test and query dataloaders for market
dataset, default=64
--checkpoints_path CHECKPOINTS_PATH
folder to output model checkpoints, default="."
--cuda enables cuda
--dropout_prob DROPOUT_PROB
probability of dropout, default=0.7
--lr LR learning rate, default=1e-4
--lr_fc LR_FC learning rate to train fc layer, default=1e-1
--manual_seed MANUAL_SEED
manual seed
--market calculate rank1 and mAP on Market dataset; dataroot
should contain folders "bounding_box_train",
"bounding_box_test", "query"
--nbins NBINS number of bins in histograms, default=150
--nepoch NEPOCH number of epochs to train, default=150
--nepoch_fc NEPOCH_FC
number of epochs to train fc layer, default=0
--nworkers NWORKERS number of data loading workers, default=10
--visdom_port VISDOM_PORT
port for visdom visualization
$ #start visdom server
$ python -m visdom.server -port 8099
$ python main.py
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