Understanding pipeline.config

I am trying transfer learning on a pre trained model present in the TensorFlow 2 Detection Model Zoo using my own custom data.

I was looking into pipeline.config file on one of the models centernet_hg104_512x512_coco17_tpu-8

This is it's content

model {
  center_net {
    num_classes: 90
    feature_extractor {
      type: "hourglass_104"
      channel_means: 104.01361846923828
      channel_means: 114.03422546386719
      channel_means: 119.91659545898438
      channel_stds: 73.60276794433594
      channel_stds: 69.89082336425781
      channel_stds: 70.91507720947266
      bgr_ordering: true
    }
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 512
        max_dimension: 512
        pad_to_max_dimension: true
      }
    }
    object_detection_task {
      task_loss_weight: 1.0
      offset_loss_weight: 1.0
      scale_loss_weight: 0.10000000149011612
      localization_loss {
        l1_localization_loss {
        }
      }
    }
    object_center_params {
      object_center_loss_weight: 1.0
      classification_loss {
        penalty_reduced_logistic_focal_loss {
          alpha: 2.0
          beta: 4.0
        }
      }
      min_box_overlap_iou: 0.699999988079071
      max_box_predictions: 100
    }
  }
}
train_config {
  batch_size: 128
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    random_crop_image {
      min_aspect_ratio: 0.5
      max_aspect_ratio: 1.7000000476837158
      random_coef: 0.25
    }
  }
  data_augmentation_options {
    random_adjust_hue {
    }
  }
  data_augmentation_options {
    random_adjust_contrast {
    }
  }
  data_augmentation_options {
    random_adjust_saturation {
    }
  }
  data_augmentation_options {
    random_adjust_brightness {
    }
  }
  data_augmentation_options {
    random_absolute_pad_image {
      max_height_padding: 200
      max_width_padding: 200
      pad_color: 0.0
      pad_color: 0.0
      pad_color: 0.0
    }
  }
  optimizer {
    adam_optimizer {
      learning_rate {
        manual_step_learning_rate {
          initial_learning_rate: 0.0010000000474974513
          schedule {
            step: 90000
            learning_rate: 9.999999747378752e-05
          }
          schedule {
            step: 120000
            learning_rate: 9.999999747378752e-06
          }
        }
      }
      epsilon: 1.0000000116860974e-07
    }
    use_moving_average: false
  }
  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED"
  num_steps: 140000
  max_number_of_boxes: 100
  unpad_groundtruth_tensors: false
  fine_tune_checkpoint_type: "detection"
  fine_tune_checkpoint_version: V2
}
train_input_reader {
  label_map_path: "PATH_TO_BE_CONFIGURED"
  tf_record_input_reader {
    input_path: "PATH_TO_BE_CONFIGURED"
  }
}
eval_config {
  metrics_set: "coco_detection_metrics"
  use_moving_averages: false
  batch_size: 1
}
eval_input_reader {
  label_map_path: "PATH_TO_BE_CONFIGURED"
  shuffle: false
  num_epochs: 1
  tf_record_input_reader {
    input_path: "PATH_TO_BE_CONFIGURED"
  }
}

Inside model block

I want to train only one class so num_classes:1, I hope i am correct here.

Inside train_config block

1. I want to know know what is the relationship between batch_size: 128 and num_steps: 140000
   Is it like Total_Number_of_training_image = batch_size x num_steps
   So in my current case I have 1510 total training images
   does this imply if i keep the batch_size: 128
num_steps = Total_Number_of_training_image / batch_size
num_steps = 1510/128 = 11.79(approx 12)

2. How do I change the learning rate scheduler

optimizer {
    adam_optimizer {
      learning_rate {
        manual_step_learning_rate {
          initial_learning_rate: 0.0010000000474974513
          schedule {
            step: 90000
            learning_rate: 9.999999747378752e-05
          }
          schedule {
            step: 120000
            learning_rate: 9.999999747378752e-06
          }
        }
      }
      epsilon: 1.0000000116860974e-07
    }
use_moving_average: false
}

Module: tf.keras.optimizers.schedules

Or instead of this should I use Transfer learning and fine-tuning

How can I pick a TensorFlow 2 Detection Model Zoo model as my Base model

I tried looking around for it's explanation found this Tensorflow object detection config files documentation

Which lead me to this Configuring the Object Detection Training Pipeline

I want to experiment with different learning rate schedules Module: tf.keras.optimizers.schedules for optimisation and select different models from TensorFlow 2 Detection Model Zoo is there a guide for me to look into as to how to do this?

I am confused