def METHOD_NAME(self, check_content=False, progress_callback=None, progress_interval=100, **kwargs):
    image_annotations = read_txt(self.annotation_file)
    content_errors = None if not check_content else []
    image_ids = [image_id for image_id, line in enumerate(image_annotations) if '.jpg' in line]
    meta = self.get_meta()
    annotations = []
    num_iterations = len(image_ids)
    for index, image_id in enumerate(image_ids):
        identifier = image_annotations[image_id]
        if check_content:
            if not check_file_existence(self.images_dir / identifier):
                content_errors.append('{}: does not exist'.format(self.images_dir / identifier))
        bbox_count = image_annotations[image_id + 1]
        bbox_lines = image_annotations[image_id + 2:image_id + 2 + int(bbox_count)]
        x_mins, y_mins, x_maxs, y_maxs = [], [], [], []
        for bbox in bbox_lines:
            x_min, y_min, x_max, y_max = convert_bboxes_xywh_to_x1y1x2y2(*(map(float, (bbox.split(' ')[0:4]))))
            x_mins.append(x_min)
            y_mins.append(y_min)
            x_maxs.append(x_max)
            y_maxs.append(y_max)
        annotations.append(DetectionAnnotation(
            identifier, [self.label_start] * len(x_mins),
            x_mins, y_mins, x_maxs, y_maxs
        ))
        if progress_callback and index % progress_interval == 0:
            progress_callback(index * 100 / num_iterations)
    return ConverterReturn(annotations, meta, content_errors)