code: def filter_positive_detections(detections):
          
          class_idx = 0
          assert(isinstance(detections, mx.nd.NDArray) or isinstance(detections, np.ndarray))
          detections_per_image = []
          # for each image
          for i in range(detections.shape[0]):
              result = []
              det = detections[i, :, :]
              for obj in det:
                  if obj[class_idx] >= 0:
                      result.append(obj)
              detections_per_image.append(result)
          logging.info("%d positive detections", len(result))
          return detections_per_image
docs: First column (class id) is -1 for negative detections
          :param detections:
          :return: