Python 标签上显示「；不适用；在Tensorflow对象识别结果中

我正在开发一个基本模型，以识别我的DGT半人马国际象棋棋盘的电子墨水显示

标识显示的边界框的大多数标签为“不适用”。在一些情况下，边界框使用“显示”标签，但这是当我在图像中识别出我身体的一部分时

我假设我的标签配置不正确，但我不确定如何调试。请参见下面的my labelmap.pbtxt以及我的培训配置

谢谢你的帮助

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示例图像： 我的身体标有“显示”：

我的身体标有“显示器”，而电路板/显示器标有“不适用”：

标有“不适用”的显示器：

---

labelmap.pbtxt

faster\u rcnn\u inception\u v2\u pets.config

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不确定您是否已解决此问题，我的2美分-此类不适用问题出现在：

labels.pbtxt文件有问题

配置文件中的类数不正确

最后但并非最不重要的一点是，用于显示带有标签的图像的python代码（例如predict_image.py）可能包含错误数量的类

item {
    id: 1
    name: 'display'
    display_name: 'display'
}

# 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: 1
    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: {
        manual_step_learning_rate {
          initial_learning_rate: 0.0002
          schedule {
            step: 900000
            learning_rate: .00002
          }
          schedule {
            step: 1200000
            learning_rate: .000002
          }
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  gradient_clipping_by_norm: 10.0
  fine_tune_checkpoint: "/Users/cs/GitHub/dgt-centaur-transcriber/models/research/object_detection/faster_rcnn_inception_v2_coco_2018_01_28/model.ckpt"
  from_detection_checkpoint: true
  load_all_detection_checkpoint_vars: 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: 200000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
}


train_input_reader: {
  tf_record_input_reader {
    input_path: "/Users/cs/GitHub/dgt-centaur-transcriber/models/research/object_detection/train.record"
  }
  label_map_path: "/Users/cs/GitHub/dgt-centaur-transcriber/models/research/object_detection/training/labelmap.pbtxt"
}

eval_config: {
  metrics_set: "coco_detection_metrics"
  num_examples: 1
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "/Users/cs/GitHub/dgt-centaur-transcriber/models/research/object_detection/test.record"
  }
  label_map_path: "/Users/cs/GitHub/dgt-centaur-transcriber/models/research/object_detection/training/labelmap.pbtxt"
  shuffle: true
  num_readers: 1
}