经过统计学上的成功训练后，结果无法通过实时摄像机反映出来。如何解决此问题？

Question

我一直在Linux上使用TensorFlow进行牛奶纸箱检测。使用的语言是python。使用的模型是fast_rcnn_inception_v2_pets。我是机器学习的新手。这是我第一次学习如何进行物体检测，请帮忙！

[我们的数据集：我们已经在相同的环境（冰箱架子）中分别拍摄了每个牛奶纸箱的照片（每个牛奶纸箱约130张照片）。我们已经拍摄了多个牛奶纸盒的混合照片（约400张照片）。

这里有证据表明该培训据说是成功的：Evidence 1Evidence 2Evidence 3

这是我们给纸箱加标签的方式：Using OpenLabeler

这里是实时摄像机供稿，它不准确（问题）：Evidence

代码：

# Faster R-CNN with Inception v2, configured for Oxford-IIIT Pets Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.

model {
  faster_rcnn {
    num_classes: 6
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 600
        max_dimension: 1024
      }
    }
    feature_extractor {
      type: 'faster_rcnn_inception_v2'
      first_stage_features_stride: 16
    }
    first_stage_anchor_generator {
      grid_anchor_generator {
        scales: [0.25, 0.5, 1.0, 2.0]
        aspect_ratios: [0.5, 1.0, 2.0]
        height_stride: 16
        width_stride: 16
      }
    }
    first_stage_box_predictor_conv_hyperparams {
      op: CONV
      regularizer {
        l2_regularizer {
          weight: 0.0
        }
      }
      initializer {
        truncated_normal_initializer {
          stddev: 0.01
        }
      }
    }
    first_stage_nms_score_threshold: 0.0
    first_stage_nms_iou_threshold: 0.7
    first_stage_max_proposals: 300
    first_stage_localization_loss_weight: 2.0
    first_stage_objectness_loss_weight: 1.0
    initial_crop_size: 14
    maxpool_kernel_size: 2
    maxpool_stride: 2
    second_stage_box_predictor {
      mask_rcnn_box_predictor {
        use_dropout: false
        dropout_keep_probability: 1.0
        fc_hyperparams {
          op: FC
          regularizer {
            l2_regularizer {
              weight: 0.0
            }
          }
          initializer {
            variance_scaling_initializer {
              factor: 1.0
              uniform: true
              mode: FAN_AVG
            }
          }
        }
      }
    }
    second_stage_post_processing {
      batch_non_max_suppression {
        score_threshold: 0.0
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 300
      }
      score_converter: SOFTMAX
    }
    second_stage_localization_loss_weight: 2.0
    second_stage_classification_loss_weight: 1.0
  }
}

train_config: {
  batch_size: 1
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        exponential_decay_learning_rate {
          initial_learning_rate: 0.0002
          decay_steps: 5000
          decay_factor: 0.9
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  gradient_clipping_by_norm: 10.0
  fine_tune_checkpoint: "/home/konbini/tensorflow1/models/research/object_detection/faster_rcnn_inception_v2_coco_2018_01_28/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 pets dataset. This
  # effectively bypasses the learning rate schedule (the learning rate will
  # never decay). Remove the below line to train indefinitely.
  num_steps: 150000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
}


train_input_reader: {
  tf_record_input_reader {
    input_path: "/home/tensorflow1/models/research/object_detection/train.record"
  }
  label_map_path: "/home/tensorflow1/models/research/object_detection/training/labelmap.pbtxt"
}

eval_config: {
  num_examples: 288
  # Number of images in testing folder
  # Note: The below line limits the evaluation process to 10 evaluations.
  # Remove the below line to evaluate indefinitely.
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "/home/tensorflow1/models/research/object_detection/test.record"
  }
  label_map_path: "/home/tensorflow1/models/research/object_detection/training/labelmap.pbtxt"
  shuffle: false
  num_readers: 1
}

当我们对任何一种颜色更改进行增强时，结果都会变得更糟。当我们对任何类型的旋转进行增强时，结果都是相同的。 Evidence

我有两个问题：我们的数据集有问题吗？我们的贴标签方法有什么问题吗？

Answer 1

这可能是因为您的模型过度适合于训练数据集，因此泛化程度不足以处理测试数据集。我可以看到，除了配置文件中的random_horizontal_flip之外，您没有使用增强技术，这可以更好地推广模型。您可以在train_config部分下添加增强技术，如下所示：

train_config: {
  ...
  data_augmentation_options {
    random_rotation90 {
    }
  }
  data_augmentation_options {
    random_distort_color {
    }
  }
  data_augmentation_options {
    random_adjust_brightness {
    }
  }
  data_augmentation_options {
    random_adjust_contrast {
    }
  }
  .....
}

所有可用的增强技术都可以在preprocessor.proto中找到：