def __init__(self, classes, num_layers=101, pretrained=False, class_agnostic=False): self.model_path = 'data/pretrained_model/detnet59.pth' self.pretrained = pretrained self.class_agnostic = class_agnostic self.dout_base_model = 256 _FPN.__init__(self, classes, class_agnostic)
def __init__(self, classes, num_layers=101, pretrained=False, class_agnostic=False): self.model_path = '/home/lab30202/lq/ai_future/galaxy_star_detect/fpn_galaxy_star/data/pretrained_model/best_galaxy_star_IN.pth' self.dout_base_model = 64 ## 256 -> 24 self.pretrained = pretrained self.class_agnostic = class_agnostic _FPN.__init__(self, classes, class_agnostic)
def __init__(self, classes, num_layers=101, pretrained=False, class_agnostic=False): self.model_path = 'data/pretrained_model/resnet101_caffe.pth' self.dout_base_model = 256 self.pretrained = pretrained self.class_agnostic = class_agnostic _FPN.__init__(self, classes, class_agnostic)
def __init__(self, classes, num_layers=50, pretrained=False, class_agnostic=False): self.model_path = 'data/pretrained_model/seresnet50-60a8950a85b2b.pkl' self.dout_base_model = 256 self.pretrained = pretrained self.class_agnostic = class_agnostic self.num_classes = len(classes) _FPN.__init__(self, classes, class_agnostic)
def __init__(self, classes, num_layers=101, pretrained=False, class_agnostic=False): self.num_layers = num_layers self.model_path = './pretrained/resnet{}_caffe.pth'.format( self.num_layers) self.dout_base_model = 256 # different self.pretrained = pretrained self.class_agnostic = class_agnostic _FPN.__init__(self, classes, class_agnostic)
def __init__(self, classes, num_layers=101, pretrained=False, class_agnostic=False, use_RCNN_sigmoid=False, att_type=None): self.model_path = 'data/pretrained_model/resnet%d_caffe.pth' % num_layers self.dout_base_model = 256 self.pretrained = pretrained self.class_agnostic = class_agnostic self.num_layers = num_layers self.att_type = att_type if use_RCNN_sigmoid: raise AssertionError('use_RCNN_sigmoid is not supported!') _FPN.__init__(self, classes, class_agnostic)