'confidence_threshold': 0.01, 'code_type': code_type, } # parameters for evaluating detection results. det_eval_param = { 'num_classes': num_classes, 'background_label_id': background_label_id, 'overlap_threshold': 0.5, 'evaluate_difficult_gt': False, 'name_size_file': name_size_file, } ### Hopefully you don't need to change the following ### # Check file. check_if_exist(train_data) check_if_exist(test_data) check_if_exist(label_map_file) check_if_exist(pretrain_model) make_if_not_exist(save_dir) make_if_not_exist(job_dir) make_if_not_exist(snapshot_dir) # Create train net. net = caffe.NetSpec() net.data, net.label = CreateAnnotatedDataLayer( train_data, batch_size=batch_size_per_device, train=True, output_label=True, label_map_file=label_map_file,
'confidence_threshold': 0.01, 'code_type': code_type, } # parameters for evaluating detection results. det_eval_param = { 'num_classes': num_classes, 'background_label_id': background_label_id, 'overlap_threshold': 0.5, 'evaluate_difficult_gt': False, 'name_size_file': name_size_file, } ### Hopefully you don't need to change the following ### # Check file. check_if_exist(train_data) check_if_exist(test_data) check_if_exist(label_map_file) check_if_exist(pretrain_model) make_if_not_exist(save_dir) make_if_not_exist(job_dir) make_if_not_exist(snapshot_dir) # Create train net. net = caffe.NetSpec() net.data, net.label = CreateAnnotatedDataLayer(train_data, batch_size=batch_size_per_device, train=True, output_label=True, label_map_file=label_map_file, transform_param=train_transform_param, batch_sampler=batch_sampler) NetBuilder(net, from_layer='data')
'confidence_threshold': 0.01, 'code_type': code_type, } # parameters for evaluating detection results. det_eval_param = { 'num_classes': num_classes, 'background_label_id': background_label_id, 'overlap_threshold': 0.5, 'evaluate_difficult_gt': False, 'name_size_file': name_size_file, } ### Hopefully you don't need to change the following ### # Check file. check_if_exist(train_data) check_if_exist(test_data) check_if_exist(label_map_file) #check_if_exist(pretrain_model) make_if_not_exist(save_dir) make_if_not_exist(job_dir) make_if_not_exist(snapshot_dir) # Create train net. net = caffe.NetSpec() net.data, net.label = CreateAnnotatedDataLayer( train_data, batch_size=batch_size_per_device, train=True, output_label=True, label_map_file=label_map_file,