Правильные настройки конфигурации для Tensorflow Object Detection API для добавления класса или передачи обучения

Question

Необходимо знать правильные параметры конфигурации для API обнаружения объектов Tensorflow, чтобы добавить класс и выполнить обучение

После прочтения https://github.com/tensorflow/models/issues/6479 и API обнаружения объектов Tensorflow Retrain до сих пор неясно, как выполнить обучение с помощью API.

Я ищу правильный способ добавить класс в обученную модель. Например, SSD с Mobilenet v1

Методы, которые я видел при использовании API обнаружения объектов, включают в себя следующие изменения: В файле конфигурации конвейера:

1. Изменить num_classes: 90 на num_classes: 1
2. Измените точную отметку_тюни: на "../ yourlocalpath / model.ckpt
3. Keep from_detection_checkpoint: true
4. Измените train_input_reader / input_path: на "../ yourtrainimagepath / train.record"
5. Измените train_input_reader / label_map_path на "../ yourlocalpath / classes.pbtxt"
6. Измените eval_input_reader / input_path на "../ yourtestimagepath / test.rocord"
7. Измените eval_input_reader / label_map_path на "../ yourlocalpath / classes.pbtxt"

Кроме того,

Измените файл: "../ yourlocalpath / classes.pbtxt" , чтобы он содержал только:

item {
id: 1
name: 'some_new_class'
}

Я тренировал 600 изображений за 200 000 шагов (18 часов) с потерей 1,5.

Я достиг более 90% точности данных тренировки, но менее 10% оценки. Это было явно нарядом. Первым делом я понял, что модель слишком сложна для одного предмета. Он просто запомнил данные тренировки. Я также заметил, что остальные 90 оригинальных предметов больше не были найдены.

Затем я изменяю num_classes на 91 и просто добавляю вещь { id: 91 имя: 'some_new_class' } в исходный файл classes.pbtxt?

Мои результаты не сильно улучшились (20%). (На этот раз я прекратил тренироваться около 100 000 шагов, но к этому моменту кривая обучения почти сгладилась).

В обоих случаях я решил не изменять "from_detection_checkpoint: true" . потому что «запуск с контрольной точки обнаружения обычно приводит к более быстрой учебной работе, чем контрольная точка классификации». ссылка: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/configuring_jobs.md#model-parameter-initialization

Как правильно обучить детектор объектов обнаруживать все объекты (старые и новые)?

Я ожидаю, что, когда я выполняю прогноз на изображении, содержащем уже обученные объекты в дополнение к моему новому объекту, все будут найдены.

Вот используемые файлы конфигурации.

1-й с num_classes: 1

# SSD with Mobilenet v1, 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 {
  ssd {
    num_classes: 1
    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
      }
    }
    image_resizer {
      fixed_shape_resizer {
        height: 300
        width: 300
      }
    }
    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: 1
        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
            }
          }
          batch_norm {
            train: true,
            scale: true,
            center: true,
            decay: 0.9997,
            epsilon: 0.001,
          }
        }
      }
    }
    feature_extractor {
      type: 'ssd_mobilenet_v1'
      min_depth: 16
      depth_multiplier: 1.0
      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 {
        }
      }
      localization_loss {
        weighted_smooth_l1 {
        }
      }
      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: 10
  optimizer {
    rms_prop_optimizer: {
      learning_rate: {
        exponential_decay_learning_rate {
          initial_learning_rate: 0.004
          decay_steps: 800720
          decay_factor: 0.95
        }
      }
      momentum_optimizer_value: 0.9
      decay: 0.9
      epsilon: 1.0
    }
  }
  fine_tune_checkpoint: "/home/adriansr/HoodML/Datasets/ssd_mobilenet_v1_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 {
    }
  }
  data_augmentation_options {
    ssd_random_crop {
    }
  }
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "/home/adriansr/HoodML/Datasets/2016_USATF_Sprint_TrainingDataset/Analyze/train.record"
  }
  label_map_path: "/home/adriansr/HoodML/hoodbibod/training/classes.pbtxt"
}

eval_config: {
  metrics_set: "coco_detection_metrics"
  num_examples: 1100
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "/home/adriansr/HoodML/Datasets/2016_USATF_Sprint_TrainingDataset/Analyze/test.record"
  }
  label_map_path: "/home/adriansr/HoodML/hoodbibod/training/classes.pbtxt"
  shuffle: false
  num_readers: 1
}

2-й с num_classes: 91

# SSD with Mobilenet v1, 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 {
  ssd {
    num_classes: 91
    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
      }
    }
    image_resizer {
      fixed_shape_resizer {
        height: 300
        width: 300
      }
    }
    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: 1
        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
            }
          }
          batch_norm {
            train: true,
            scale: true,
            center: true,
            decay: 0.9997,
            epsilon: 0.001,
          }
        }
      }
    }
    feature_extractor {
      type: 'ssd_mobilenet_v1'
      min_depth: 16
      depth_multiplier: 1.0
      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 {
        }
      }
      localization_loss {
        weighted_smooth_l1 {
        }
      }
      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: 10
  optimizer {
    rms_prop_optimizer: {
      learning_rate: {
        exponential_decay_learning_rate {
          initial_learning_rate: 0.004
          decay_steps: 800720
          decay_factor: 0.95
        }
      }
      momentum_optimizer_value: 0.9
      decay: 0.9
      epsilon: 1.0
    }
  }
  fine_tune_checkpoint: "/home/adriansr/HoodML/Datasets/ssd_mobilenet_v1_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 {
    }
  }
  data_augmentation_options {
    ssd_random_crop {
    }
  }
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "/home/adriansr/HoodML/Datasets/2016_USATF_Sprint_TrainingDataset/Analyze/train.record"
  }
  label_map_path: "/home/adriansr/HoodML/hoodbibod/training/mscoco_complete_label_map_with_bib.pbtxt"
}

eval_config: {
  metrics_set: "coco_detection_metrics"
  num_examples: 1100
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "/home/adriansr/HoodML/Datasets/2016_USATF_Sprint_TrainingDataset/Analyze/test.record"
  }
  label_map_path: "/home/adriansr/HoodML/hoodbibod/training/mscoco_complete_label_map_with_bib.pbtxt"
  shuffle: false
  num_readers: 1
}

classes.pbtxt

item {
  id: 1
  name: 'Bib'
}

mscoco_complete_label_map_with_bib.pbtxt

item {
  name: "background"
  id: 0
  display_name: "background"
}
item {
  name: "/m/01g317"
  id: 1
  display_name: "person"
}
item {
  name: "/m/0199g"
  id: 2
  display_name: "bicycle"
}
item {
  name: "/m/0k4j"
  id: 3
  display_name: "car"
}
item {
  name: "/m/04_sv"
  id: 4
  display_name: "motorcycle"
}
item {
  name: "/m/05czz6l"
  id: 5
  display_name: "airplane"
}
item {
  name: "/m/01bjv"
  id: 6
  display_name: "bus"
}
item {
  name: "/m/07jdr"
  id: 7
  display_name: "train"
}
item {
  name: "/m/07r04"
  id: 8
  display_name: "truck"
}
item {
  name: "/m/019jd"
  id: 9
  display_name: "boat"
}
item {
  name: "/m/015qff"
  id: 10
  display_name: "traffic light"
}
item {
  name: "/m/01pns0"
  id: 11
  display_name: "fire hydrant"
}
item {
  name: "12"
  id: 12
  display_name: "12"
}
item {
  name: "/m/02pv19"
  id: 13
  display_name: "stop sign"
}
item {
  name: "/m/015qbp"
  id: 14
  display_name: "parking meter"
}
item {
  name: "/m/0cvnqh"
  id: 15
  display_name: "bench"
}
item {
  name: "/m/015p6"
  id: 16
  display_name: "bird"
}
item {
  name: "/m/01yrx"
  id: 17
  display_name: "cat"
}
item {
  name: "/m/0bt9lr"
  id: 18
  display_name: "dog"
}
item {
  name: "/m/03k3r"
  id: 19
  display_name: "horse"
}
item {
  name: "/m/07bgp"
  id: 20
  display_name: "sheep"
}
item {
  name: "/m/01xq0k1"
  id: 21
  display_name: "cow"
}
item {
  name: "/m/0bwd_0j"
  id: 22
  display_name: "elephant"
}
item {
  name: "/m/01dws"
  id: 23
  display_name: "bear"
}
item {
  name: "/m/0898b"
  id: 24
  display_name: "zebra"
}
item {
  name: "/m/03bk1"
  id: 25
  display_name: "giraffe"
}
item {
  name: "26"
  id: 26
  display_name: "26"
}
item {
  name: "/m/01940j"
  id: 27
  display_name: "backpack"
}
item {
  name: "/m/0hnnb"
  id: 28
  display_name: "umbrella"
}
item {
  name: "29"
  id: 29
  display_name: "29"
}
item {
  name: "30"
  id: 30
  display_name: "30"
}
item {
  name: "/m/080hkjn"
  id: 31
  display_name: "handbag"
}
item {
  name: "/m/01rkbr"
  id: 32
  display_name: "tie"
}
item {
  name: "/m/01s55n"
  id: 33
  display_name: "suitcase"
}
item {
  name: "/m/02wmf"
  id: 34
  display_name: "frisbee"
}
item {
  name: "/m/071p9"
  id: 35
  display_name: "skis"
}
item {
  name: "/m/06__v"
  id: 36
  display_name: "snowboard"
}
item {
  name: "/m/018xm"
  id: 37
  display_name: "sports ball"
}
item {
  name: "/m/02zt3"
  id: 38
  display_name: "kite"
}
item {
  name: "/m/03g8mr"
  id: 39
  display_name: "baseball bat"
}
item {
  name: "/m/03grzl"
  id: 40
  display_name: "baseball glove"
}
item {
  name: "/m/06_fw"
  id: 41
  display_name: "skateboard"
}
item {
  name: "/m/019w40"
  id: 42
  display_name: "surfboard"
}
item {
  name: "/m/0dv9c"
  id: 43
  display_name: "tennis racket"
}
item {
  name: "/m/04dr76w"
  id: 44
  display_name: "bottle"
}
item {
  name: "45"
  id: 45
  display_name: "45"
}
item {
  name: "/m/09tvcd"
  id: 46
  display_name: "wine glass"
}
item {
  name: "/m/08gqpm"
  id: 47
  display_name: "cup"
}
item {
  name: "/m/0dt3t"
  id: 48
  display_name: "fork"
}
item {
  name: "/m/04ctx"
  id: 49
  display_name: "knife"
}
item {
  name: "/m/0cmx8"
  id: 50
  display_name: "spoon"
}
item {
  name: "/m/04kkgm"
  id: 51
  display_name: "bowl"
}
item {
  name: "/m/09qck"
  id: 52
  display_name: "banana"
}
item {
  name: "/m/014j1m"
  id: 53
  display_name: "apple"
}
item {
  name: "/m/0l515"
  id: 54
  display_name: "sandwich"
}
item {
  name: "/m/0cyhj_"
  id: 55
  display_name: "orange"
}
item {
  name: "/m/0hkxq"
  id: 56
  display_name: "broccoli"
}
item {
  name: "/m/0fj52s"
  id: 57
  display_name: "carrot"
}
item {
  name: "/m/01b9xk"
  id: 58
  display_name: "hot dog"
}
item {
  name: "/m/0663v"
  id: 59
  display_name: "pizza"
}
item {
  name: "/m/0jy4k"
  id: 60
  display_name: "donut"
}
item {
  name: "/m/0fszt"
  id: 61
  display_name: "cake"
}
item {
  name: "/m/01mzpv"
  id: 62
  display_name: "chair"
}
item {
  name: "/m/02crq1"
  id: 63
  display_name: "couch"
}
item {
  name: "/m/03fp41"
  id: 64
  display_name: "potted plant"
}
item {
  name: "/m/03ssj5"
  id: 65
  display_name: "bed"
}
item {
  name: "66"
  id: 66
  display_name: "66"
}
item {
  name: "/m/04bcr3"
  id: 67
  display_name: "dining table"
}
item {
  name: "68"
  id: 68
  display_name: "68"
}
item {
  name: "69"
  id: 69
  display_name: "69"
}
item {
  name: "/m/09g1w"
  id: 70
  display_name: "toilet"
}
item {
  name: "71"
  id: 71
  display_name: "71"
}
item {
  name: "/m/07c52"
  id: 72
  display_name: "tv"
}
item {
  name: "/m/01c648"
  id: 73
  display_name: "laptop"
}
item {
  name: "/m/020lf"
  id: 74
  display_name: "mouse"
}
item {
  name: "/m/0qjjc"
  id: 75
  display_name: "remote"
}
item {
  name: "/m/01m2v"
  id: 76
  display_name: "keyboard"
}
item {
  name: "/m/050k8"
  id: 77
  display_name: "cell phone"
}
item {
  name: "/m/0fx9l"
  id: 78
  display_name: "microwave"
}
item {
  name: "/m/029bxz"
  id: 79
  display_name: "oven"
}
item {
  name: "/m/01k6s3"
  id: 80
  display_name: "toaster"
}
item {
  name: "/m/0130jx"
  id: 81
  display_name: "sink"
}
item {
  name: "/m/040b_t"
  id: 82
  display_name: "refrigerator"
}
item {
  name: "83"
  id: 83
  display_name: "83"
}
item {
  name: "/m/0bt_c3"
  id: 84
  display_name: "book"
}
item {
  name: "/m/01x3z"
  id: 85
  display_name: "clock"
}
item {
  name: "/m/02s195"
  id: 86
  display_name: "vase"
}
item {
  name: "/m/01lsmm"
  id: 87
  display_name: "scissors"
}
item {
  name: "/m/0kmg4"
  id: 88
  display_name: "teddy bear"
}
item {
  name: "/m/03wvsk"
  id: 89
  display_name: "hair drier"
}
item {
  name: "/m/012xff"
  id: 90
  display_name: "toothbrush"
}
item {
  name: "/m/bib"
  id: 91
  display_name: "bib"
}