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Param_Config.py
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Param_Config.py
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from collections import OrderedDict
import os.path as osp
# baseline softmax
param_base = \
{
# system fix params
'workers': 4,
'split': 0,
'height': 256,
'width': 128,
'data_dir': osp.join(osp.dirname(osp.abspath(__file__)), 'data'),
'logs_dir': osp.join(osp.dirname(osp.abspath(__file__)), 'logs'),
'dataset': 'market1501', # 'cuhk03' 'market1501' 'MSMT17_V1'
'arch': 'resnet50',
'momentum': 0.9,
'weight_decay': 5e-4,
# display and evaluate
'resume': False,
'evaluate': False,
'start_save': 0,
'step_size': 35,
'epochs': 120,
'print_freq': 20,
'W_std': 0.001,
'loss': 'softmax', # 'softmax', 'hingeloss', 'std_hingeloss'
# some basis hyper-params
'batch_size': 512,
'combine_trainval': True,
'features': 0,
'dropout': 0.5,
'lr': 0.1,
# hyper-params
'gamma': 1.0,
'F_norm': True,
'W_norm': True,
'norm_test': False,
'CCS' : False,
'up_term' : False,
# pretrained
'pretrained': True,
}
# metric triplet
param_metric_triplet = \
{
# system fix params
'workers': 4,
'split': 0,
'height': 256,
'width': 128,
'data_dir': osp.join(osp.dirname(osp.abspath(__file__)), 'data'),
'logs_dir': osp.join(osp.dirname(osp.abspath(__file__)), 'logs'),
'dataset': 'market1501', # 'cuhk03' 'market1501' 'MSMT17_V1'
'arch': 'resnet50',
'dist_metric': 'tmm',
'momentum': 0.9,
'weight_decay': 5e-4,
# display and evaluate
'resume': False,
'evaluate': False,
'start_save': 0,
'step_size': 35,
'epochs': 45,
'print_freq': 20,
'W_std': 0.001,
'loss': 'softmax', # 'softmax', 'hingeloss', 'std_hingeloss'
# some basis hyper-params
'num_instances': 4,
'batch_size': 256,
'combine_trainval': True,
'features': 128,
'dropout': 0,
'lr': 0.0002,
# hyper-params
'gamma': 1.0,
# 'F_norm': False,
# 'W_norm': False,
'norm_test': False,
'norm_train': False,
'margin': 0.5,
}