在 PASCALVOC 上从模型动物园微调 EfficientDet-D0 无法识别 class 标签 1（TensorFlow 对象检测 API）

Question

我已经从对象检测 API 模型动物园下载了 EfficientDet D0 512x512 模型，下载了 PASCAL VOC 数据集并使用 create_pascal_tf_record.py 文件对其进行了预处理。接下来，我获取了其中一个配置文件并对其进行了调整以适应架构和 VOC 数据集。当使用 pascal_voc_detection_metrics 评估生成的网络时，它给我第一个 class（飞机）接近零的 mAP，其他 classes 表现良好。我假设我在配置文件中的一项设置是错误的（粘贴在下面），为什么会发生这种情况，我该如何解决？

model {
  ssd {
    inplace_batchnorm_update: true
    freeze_batchnorm: false
    num_classes: 20
    add_background_class: false
    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
        use_matmul_gather: true
      }
    }
    similarity_calculator {
      iou_similarity {
      }
    }
    encode_background_as_zeros: true
    anchor_generator {
      multiscale_anchor_generator {
        min_level: 3
        max_level: 7
        anchor_scale: 4.0
        aspect_ratios: [1.0, 2.0, 0.5]
        scales_per_octave: 3
      }
    }
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 512
        max_dimension: 512
        pad_to_max_dimension: true
        }
    }
    box_predictor {
      weight_shared_convolutional_box_predictor {
        depth: 64
        class_prediction_bias_init: -4.6
        conv_hyperparams {
          force_use_bias: true
          activation: SWISH
          regularizer {
            l2_regularizer {
              weight: 0.00004
            }
          }
          initializer {
            random_normal_initializer {
              stddev: 0.01
              mean: 0.0
            }
          }
          batch_norm {
            scale: true
            decay: 0.99
            epsilon: 0.001
          }
        }
        num_layers_before_predictor: 3
        kernel_size: 3
        use_depthwise: true
      }
    }
    feature_extractor {
      type: 'ssd_efficientnet-b0_bifpn_keras'
      bifpn {
        min_level: 3
        max_level: 7
        num_iterations: 3
        num_filters: 64
      }
      conv_hyperparams {
        force_use_bias: true
        activation: SWISH
        regularizer {
          l2_regularizer {
            weight: 0.00004
          }
        }
        initializer {
          truncated_normal_initializer {
            stddev: 0.03
            mean: 0.0
          }
        }
        batch_norm {
          scale: true,
          decay: 0.99,
          epsilon: 0.001,
        }
      }
    }
    loss {
      classification_loss {
        weighted_sigmoid_focal {
          alpha: 0.25
          gamma: 1.5
        }
      }
      localization_loss {
        weighted_smooth_l1 {
        }
      }
      classification_weight: 1.0
      localization_weight: 1.0
    }
    normalize_loss_by_num_matches: true
    normalize_loc_loss_by_codesize: true
    post_processing {
      batch_non_max_suppression {
        score_threshold: 1e-8
        iou_threshold: 0.5
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SIGMOID
    }
  }
}

train_config: {
  fine_tune_checkpoint: "oracle/efficientdet_d0/checkpoint/ckpt-0"
  fine_tune_checkpoint_version: V2
  fine_tune_checkpoint_type: "detection"
  batch_size: 3
  startup_delay_steps: 0
  use_bfloat16: false
  num_steps: 30000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    random_scale_crop_and_pad_to_square {
      output_size: 512
      scale_min: 0.1
      scale_max: 2.0
    }
  }
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        cosine_decay_learning_rate {
          learning_rate_base: 8e-2
          total_steps: 30000
          warmup_learning_rate: .001
          warmup_steps: 2500
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  max_number_of_boxes: 100
  unpad_groundtruth_tensors: false
  update_trainable_variables: ["WeightSharedConvolutionalBoxPredictor"]
}

train_input_reader: {
  label_map_path: "pascalVOC/pascal_label_map.pbtxt"
  tf_record_input_reader {
    input_path: "pascalVOC/pascal_train.record"
  }
}

eval_config: {
  metrics_set: "pascal_voc_detection_metrics"
  use_moving_averages: false
  batch_size: 1;
}

eval_input_reader: {
  label_map_path: "pascalVOC/pascal_label_map.pbtxt"
  shuffle: false
  num_epochs: 1
  tf_record_input_reader {
    input_path: "pascalVOC/pascal_val.record"
  }
}

Answer 1

pascal_voc_detection_metrics 计算指标的方式存在错误，可以找到修复程序 here

在 PASCALVOC 上从模型动物园微调 EfficientDet-D0 无法识别 class 标签 1（TensorFlow 对象检测 API）

finetuning EfficientDet-D0 from model zoo on PASCALVOC doesn't recognize class label 1 (TensorFlow Object Detection API)

object-detection

tensorflow