def train_net(args):
imagedb = ImageDB(args.anno_file)
train_imdb = imagedb.load_imdb()
# train_imdb = imagedb.append_flipped_images(train_imdb)
imagedb = ImageDB(args.eval_file)
eval_imdb = imagedb.load_imdb()
print('train : %d\teval : %d' % (len(train_imdb), len(eval_imdb)))
train_pnet(args, train_imdb, eval_imdb)
def train_net(annotation_file,
model_store_path,
end_epoch=16,
frequent=200,
lr=0.01,
batch_size=128,
use_cuda=False):
imagedb = ImageDB(annotation_file)
gt_imdb = imagedb.load_imdb()
gt_imdb = imagedb.append_flipped_images(gt_imdb)
train_pnet(model_store_path=model_store_path,
end_epoch=end_epoch,
imdb=gt_imdb,
batch_size=batch_size,
frequent=frequent,
base_lr=lr,
use_cuda=use_cuda)
def train_net(annotation_file,
model_store_path,
end_epoch=16,
frequent=200,
lr=0.01,
lr_epoch_decay=[9],
batch_size=128,
use_cuda=False,
load=''):
imagedb = ImageDB(annotation_file)
gt_imdb = imagedb.load_imdb()
print('DATASIZE', len(gt_imdb))
gt_imdb = imagedb.append_flipped_images(gt_imdb)
print('FLIP DATASIZE', len(gt_imdb))
train_pnet(model_store_path=model_store_path,
end_epoch=end_epoch,
imdb=gt_imdb,
batch_size=batch_size,
frequent=frequent,
base_lr=lr,
lr_epoch_decay=lr_epoch_decay,
use_cuda=use_cuda,
load=load)