Object_Detection在损失约0.5时停止损失

Question

我遇到的问题是，当我使用Tensorflow训练我的Object_detector时，损失在0.5左右停止，当我测试它时，它显然不是很准确。

我也是从头开始训练，因为CICO数据集不包含我正在寻找的东西。

我想知道你们是否看到我在做事的任何问题。

我也有点困惑

  batch_non_max_suppression {
            score_threshold: 1e-8
            iou_threshold: 0.6
            max_detections_per_class: 100
            max_total_detections: 100
          }

和

 hard_example_miner {
            num_hard_examples: 3000
            iou_threshold: 0.99
            loss_type: CLASSIFICATION
            max_negatives_per_positive: 3
            min_negatives_per_image: 0
          }

我想知道我应该怎样改变这些

无论如何......

这是我的rocks_label_map.pbtxt

item {
  id: 1
  name: 'empty'
}

item {
  id: 2
  name: 'copper'
}

item {
  id: 3
  name: 'tin'
}

item {
  id: 4
  name: 'iron'
}

这是我的pipeline.config

model {
  ssd {
    num_classes: 4
    box_coder {
      faster_rcnn_box_coder {
        y_scale: 10.0
        x_scale: 10.0
        height_scale: 5.0
        width_scale: 5.0
      }
    }
    matcher {
      argmax_matcher {
        matched_threshold: 0.5
        unmatched_threshold: 0.5
        ignore_thresholds: false
        negatives_lower_than_unmatched: true
        force_match_for_each_row: true
      }
    }
    similarity_calculator {
      iou_similarity {
      }
    }
    anchor_generator {
      ssd_anchor_generator {
        num_layers: 6
        min_scale: 0.2
        max_scale: 0.95
        aspect_ratios: 1.0
        aspect_ratios: 2.0
        aspect_ratios: 0.5
        aspect_ratios: 3.0
        aspect_ratios: 0.3333
        reduce_boxes_in_lowest_layer: true
      }
    }
    image_resizer {
      fixed_shape_resizer {
        height: 251
        width: 382
      }
    }
    box_predictor {
      convolutional_box_predictor {
        min_depth: 0
        max_depth: 0
        num_layers_before_predictor: 0
        use_dropout: false
        dropout_keep_probability: 0.8
        kernel_size: 3
        box_code_size: 4
        apply_sigmoid_to_scores: false
        conv_hyperparams {
          activation: RELU_6,
          regularizer {
            l2_regularizer {
              weight: 0.00004
            }
          }
          initializer {
            truncated_normal_initializer {
              stddev: 0.03
              mean: 0.0
            }
          }
        }
      }
    }
    feature_extractor {
      type: 'ssd_inception_v2'
      min_depth: 16
      depth_multiplier: 1.0f
      conv_hyperparams {
        activation: RELU_6,
        regularizer {
          l2_regularizer {
            weight: 0.00004
          }
        }
        initializer {
          truncated_normal_initializer {
            stddev: 0.03
            mean: 0.0
          }
        }
        batch_norm {
          train: true,
          scale: true,
          center: true,
          decay: 0.9997,
          epsilon: 0.001,
        }
      }
    }
    loss {
      classification_loss {
        weighted_sigmoid {
          anchorwise_output: true
        }
      }
      localization_loss {
        weighted_smooth_l1 {
          anchorwise_output: true
        }
      }
      hard_example_miner {
        num_hard_examples: 3000
        iou_threshold: 0.99
        loss_type: CLASSIFICATION
        max_negatives_per_positive: 3
        min_negatives_per_image: 0
      }
      classification_weight: 1.0
      localization_weight: 1.0
    }
    normalize_loss_by_num_matches: true
    post_processing {
      batch_non_max_suppression {
        score_threshold: 1e-8
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SIGMOID
    }
  }
}

train_config: {
  batch_size: 8
  optimizer {
    rms_prop_optimizer: {
      learning_rate: {
        exponential_decay_learning_rate {
          initial_learning_rate: 0.001
          decay_steps: 800720
          decay_factor: 0.95
        }
      }
      momentum_optimizer_value: 0.9
      decay: 0.9
      epsilon: 1.0
    }
  }
  # fine_tune_checkpoint: "data/model.ckpt"
  # from_detection_checkpoint: true
  num_steps: 150000
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "rocksTrain.record"
  }
  label_map_path: "rocks_label_map.pbtxt"
}

eval_config: {
  num_examples: 200
  # Note: The below line limits the evaluation process to 10 evaluations.
  # Remove the below line to evaluate indefinitely.
  max_evals: 10
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "rocksValidation.record"
  }
  label_map_path: "rocks_label_map.pbtxt"
  shuffle: false
  num_readers: 1
}