def METHOD_NAME(self, check_content=False, progress_callback=None, progress_interval=100, **kwargs):
    content_errors = None if not check_content else []
    ann_files = self.annotation_dir.glob('*.txt')
    image_ann_pairs = [(ann.name.replace('.txt', self.images_suffix), ann.name) for ann in ann_files]
    meta = self.get_meta()
    self.reversed_label_map = {value: key for key, value in meta['label_map'].items()}
    num_iterations = len(image_ann_pairs)
    annotations = []
    for idx, (identifier, annotation_files) in enumerate(image_ann_pairs):
        labels, x_mins, y_mins, x_maxs, y_maxs = self.parse_annotation(annotation_files)
        annotations.append(DetectionAnnotation(identifier, labels, x_mins, y_mins, x_maxs, y_maxs))
        if check_content:
            if self.images_dir is None:
                self.images_dir = self.annotation_dir.parent / 'image_2'
            check_file_existence(self.images_dir / identifier)
            content_errors.append('{}: does not exist'.format(self.images_dir / identifier))
        if progress_callback and idx % progress_interval == 0:
            progress_callback(idx * 100 / num_iterations)
    return ConverterReturn(annotations, meta, content_errors)