coco ann: [u'segmentation', u'area', u'iscrowd', u'image_id', u'bbox', u'category_id', u'id']
        iscrowd:
            crowd instances are handled by marking their overlaps with all categories to -1
            and later excluded in training
        bbox:
            [x1, y1, w, h]
        :param index: coco image id
        :return: roidb entry

def _load_annotation(self, _coco, coco_ind_to_class_ind, index):
        """
        coco ann: [u'segmentation', u'area', u'iscrowd', u'image_id', u'bbox', u'category_id', u'id']
        iscrowd:
            crowd instances are handled by marking their overlaps with all categories to -1
            and later excluded in training
        bbox:
            [x1, y1, w, h]
        :param index: coco image id
        :return: roidb entry
        """
        im_ann = _coco.loadImgs(index)[0]
        filename = self._image_file_tmpl.format(im_ann['file_name'])
        width = im_ann['width']
        height = im_ann['height']

        annIds = _coco.getAnnIds(imgIds=index, iscrowd=None)
        objs = _coco.loadAnns(annIds)

        # sanitize bboxes
        valid_objs = []
        for obj in objs:
            x, y, w, h = obj['bbox']
            x1 = np.max((0, x))
            y1 = np.max((0, y))
            x2 = np.min((width - 1, x1 + np.max((0, w - 1))))
            y2 = np.min((height - 1, y1 + np.max((0, h - 1))))
            if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
                obj['clean_bbox'] = [x1, y1, x2, y2]
                valid_objs.append(obj)
        objs = valid_objs
        num_objs = len(objs)

        boxes = np.zeros((num_objs, 4), dtype=np.uint16)
        gt_classes = np.zeros((num_objs,), dtype=np.int32)
        for ix, obj in enumerate(objs):
            cls = coco_ind_to_class_ind[obj['category_id']]
            boxes[ix, :] = obj['clean_bbox']
            gt_classes[ix] = cls

        roi_rec = {'index': index,
                   'image': filename,
                   'height': height,
                   'width': width,
                   'boxes': boxes,
                   'gt_classes': gt_classes,
                   'flipped': False}
        return roi_rec