RAW_VIDEO_DIR = '../../../data/nano/' #'../input/raw/'
TRAIN_IMG_DIR = '/opt/data_fast/pri_matrix/train_img/'
TEST_VIDEO_DIR = '../../../data/nano/'  #'../input/raw_test/'
UNUSED_VIDEO_DIR = '../../../data/nano/'  #'../input/raw_unused/'
UNUSED_IMG_DIR = '../input/raw_unused_img/'
SMALL_VIDEO_DIR = '../../../data/nano/'  #'../input/small/'
TRAINING_SET_LABELS = '../input/Pri-matrix_Factorization_-_Training_Set_Labels.csv'
SUBMISSION_FORMAT = '../input/Pri-matrix_Factorization_-_Submission_Format.csv'

ALL_MODELS = [
    [('resnet50_avg', fold) for fold in [1, 2, 3, 4]],
    [('xception_avg', fold) for fold in [1, 2, 3, 4]],
    [('xception_avg_ch10', fold) for fold in [1, 2, 3, 4]],
    [('inception_v3', fold) for fold in [1, 2, 3, 4]],
    [('inception_v2_resnet', fold) for fold in [1, 2, 3, 4]],
    [('inception_v2_resnet_ch10', fold) for fold in [1, 2, 3, 4]],
    [('resnet152', fold) for fold in [1, 2, 3, 4]],
    [('inception_v2_resnet_extra', fold) for fold in [1, 2, 3, 4]],
]

MODEL_WEIGHTS = {
    'resnet50_avg': 4,
    'xception_avg': 6,
    'xception_avg_ch10': 8,
    'inception_v3': 8,
    'inception_v2_resnet': 6,
    'inception_v2_resnet_ch10': 10,
    'resnet152': 4,
    'inception_v2_resnet_extra': 20
}

f30dadc08ee2b1d457cdff6a32ae6d2910527017
<mask token>

RAW_VIDEO_DIR = '../../../data/nano/'
TRAIN_IMG_DIR = '/opt/data_fast/pri_matrix/train_img/'
TEST_VIDEO_DIR = '../../../data/nano/'
UNUSED_VIDEO_DIR = '../../../data/nano/'
UNUSED_IMG_DIR = '../input/raw_unused_img/'
SMALL_VIDEO_DIR = '../../../data/nano/'
TRAINING_SET_LABELS = (
    '../input/Pri-matrix_Factorization_-_Training_Set_Labels.csv')
SUBMISSION_FORMAT = '../input/Pri-matrix_Factorization_-_Submission_Format.csv'
ALL_MODELS = [[('resnet50_avg', fold) for fold in [1, 2, 3, 4]], [(
    'xception_avg', fold) for fold in [1, 2, 3, 4]], [('xception_avg_ch10',
    fold) for fold in [1, 2, 3, 4]], [('inception_v3', fold) for fold in [1,
    2, 3, 4]], [('inception_v2_resnet', fold) for fold in [1, 2, 3, 4]], [(
    'inception_v2_resnet_ch10', fold) for fold in [1, 2, 3, 4]], [(
    'resnet152', fold) for fold in [1, 2, 3, 4]], [(
    'inception_v2_resnet_extra', fold) for fold in [1, 2, 3, 4]]]
MODEL_WEIGHTS = {'resnet50_avg': 4, 'xception_avg': 6, 'xception_avg_ch10':
    8, 'inception_v3': 8, 'inception_v2_resnet': 6,
    'inception_v2_resnet_ch10': 10, 'resnet152': 4,
    'inception_v2_resnet_extra': 20}

RAW_VIDEO_DIR = '../../../data/nano/' #'../input/raw/'
TRAIN_IMG_DIR = '/opt/data_fast/pri_matrix/train_img/'
TEST_VIDEO_DIR = '../../../data/nano/'  #'../input/raw_test/'
UNUSED_VIDEO_DIR = '../../../data/nano/'  #'../input/raw_unused/'
UNUSED_IMG_DIR = '../input/raw_unused_img/'
SMALL_VIDEO_DIR = '../../../data/nano/'  #'../input/small/'
TRAINING_SET_LABELS = '../input/Pri-matrix_Factorization_-_Training_Set_Labels.csv'
SUBMISSION_FORMAT = '../input/Pri-matrix_Factorization_-_Submission_Format.csv'

ALL_MODELS = [
    [('resnet50_avg', fold) for fold in [1, 2, 3, 4]],
    [('xception_avg', fold) for fold in [1, 2, 3, 4]],
    [('xception_avg_ch10', fold) for fold in [1, 2, 3, 4]],
    [('inception_v3', fold) for fold in [1, 2, 3, 4]],
    [('inception_v2_resnet', fold) for fold in [1, 2, 3, 4]],
    [('inception_v2_resnet_ch10', fold) for fold in [1, 2, 3, 4]],
    [('resnet152', fold) for fold in [1, 2, 3, 4]],
    [('inception_v2_resnet_extra', fold) for fold in [1, 2, 3, 4]],
]

MODEL_WEIGHTS = {
    'resnet50_avg': 4,
    'xception_avg': 6,
    'xception_avg_ch10': 8,
    'inception_v3': 8,
    'inception_v2_resnet': 6,
    'inception_v2_resnet_ch10': 10,
    'resnet152': 4,
    'inception_v2_resnet_extra': 20
}
