def METHOD_NAME(session, oriImg):
    input_shape = (640,640)
    img, ratio = preprocess(oriImg, input_shape)
    input = img[None, :, :, :]
    outNames = session.getUnconnectedOutLayersNames()
    session.setInput(input)
    output = session.forward(outNames)
    predictions = demo_postprocess(output[0], input_shape)[0]
    boxes = predictions[:, :4]
    scores = predictions[:, 4:5] * predictions[:, 5:]
    boxes_xyxy = np.ones_like(boxes)
    boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2.
    boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3]/2.
    boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2.
    boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2.
    boxes_xyxy /= ratio
    dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)
    if dets is None:
        return None
    final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
    isscore = final_scores>0.3
    iscat = final_cls_inds == 0
    isbbox = [ i and j for (i, j) in zip(isscore, iscat)]
    final_boxes = final_boxes[isbbox]
    return final_boxes