通过Tensorflow对象检测继续评估训练

Question

使用Tensorflow object detection training，在FRCNN初始网络v2上进行培训。 培训的配置如下。

optimizer {
    momentum_optimizer: {
      learning_rate: {
        manual_step_learning_rate {
          initial_learning_rate: 0.0002
          schedule {
            step: 50000
            learning_rate: .00002
          }
          schedule {
            step: 60000
            learning_rate: .000002
          }
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  gradient_clipping_by_norm: 10.0
  fine_tune_checkpoint: "/home/itc/Data/Cheers_Store/TrainedModels/FRcnnInceptionNet/pretrained/model.ckpt"
  from_detection_checkpoint: true
  # Note: The below line limits the training process to 200K steps, which we
  # empirically found to be sufficient enough to train the COCO dataset. This
  # effectively bypasses the learning rate schedule (the learning rate will
  # never decay). Remove the below line to train indefinitely.
  num_steps: 70000

训练了70000次迭代，包含30000张图像。

具有3000张图像和设置的评估如下。

eval_config: {
  num_examples: 3000
  # Note: The below line limits the evaluation process to 10 evaluations.
  # Remove the below line to evaluate indefinitely.
  max_evals: 10
}

在训练过程中，它最多训练了4200次迭代。然后继续评估，不再进行培训。可能是什么问题？