# Experiment all tricks with center loss : 256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005
# Dataset 1: market1501
# imagesize: 256x128
# batchsize: 16x4
# warmup_step 10
# random erase prob 0.5
# labelsmooth: on
# last stride 1
# bnneck on
# with center los
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('dukemtmc')" DATASETS.ROOT_DIR "('reid_data/')" MODEL.PRETRAIN_CHOICE "('imagenet')" TEST.WEIGHT "('reid_baseline_review/Opensource_test/dukemtmc_unt/Experiment-all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005/resnet50_model_120.pth')"

# Experiment all tricks with center loss : 256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005
# Dataset 1: market1501
# imagesize: 256x128
# batchsize: 16x4
# warmup_step 10
# random erase prob 0.5
# labelsmooth: on
# last stride 1
# bnneck on
# with center los
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('dukemtmc')" DATASETS.ROOT_DIR "('reid_data/')" MODEL.PRETRAIN_CHOICE "('imagenet')" TEST.WEIGHT "('reid_baseline_review/Opensource_test/dukemtmc_unt/Experiment-all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005/resnet50_model_120.pth')"

# Experiment all tricks with center loss : 256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005 # Dataset 1: market1501 # imagesize: 256x128 # batchsize: 16x4 # warmup_step 10 # random erase prob 0.5 # labelsmooth: on # last stride 1 # bnneck on # with center los