def METHOD_NAME(self, output, img_info, idx_offset, cls_conf=0.35):
    ratio = img_info["ratio"]
    img = img_info["raw_img"]
    image_id = img_info['id']
    if output is None:
        return img
    bboxes = output[:, 0:4]
    # preprocessing: resize
    bboxes /= ratio # [[x0,y0,x1,y1], ...]
    cls = output[:, 6]
    scores = output[:, 4] * output[:, 5]
    # enumerate all bbox
    i=0
    res = []
    for box, c, s in zip(bboxes, cls, scores):
        if s < cls_conf:
            continue
        x0, y0, x1, y1 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
        w, h = x1-x0, y1-y0
        idx = idx_offset + i
        i += 1
        cat_id = int(c)
        res.append({
            'area': w * h,
            'bbox': [x0, y0, w, h],
            'category_id': cat_id,
            'id': idx,
            'image_id': image_id,
            'iscrowd': 0,
            'segmentation': [[x0, y0, x1, y1]]
        })
    return res