【Tensorflow】 Object_detection之配置Training Pipeline

参考：Configuring an object detection pipeline

1、config文件

配置好的config文件存放路径：object_detection/samples/configs

2、PASCAL VOC数据集配置

选取faster_rcnn_resnet101_voc07.config做为该数据集的config文件，并复制到对应目录，下面为该文件的内容，需要修改的部分见备注

 # Faster R-CNN with Resnet-101 (v1), configured for Pascal VOC 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: 20 # 如果是自己数据集需要修改类目数
     image_resizer {
       keep_aspect_ratio_resizer {
         min_dimension: 600
         max_dimension: 1024
       }
     }
     feature_extractor {
       type: 'faster_rcnn_resnet101'
       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 # 每次喂的数据量
   optimizer {
     momentum_optimizer: {
       learning_rate: {
         manual_step_learning_rate {
           initial_learning_rate: 0.0001
           schedule {
             step: 500000
             learning_rate: .00001
           }
           schedule {
             step: 700000
             learning_rate: .000001
           }
         }
       }
       momentum_optimizer_value: 0.9
     }
     use_moving_average: false
   }
   gradient_clipping_by_norm: 10.0
   fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"  # 是否需要加入别人预先训练好的模型，如是需要加入完整文件路径，别人预先训练好的模型可以从这找到：https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
   from_detection_checkpoint: false  # 布尔值，true为使用别人预先训练好的模型，这里暂时先不加，后面在来研究怎么去匹配
   num_steps: 800000 # 最大训练次数
   data_augmentation_options {
     random_horizontal_flip {
     }
   }
 }

 train_input_reader: {
   tf_record_input_reader {
     input_path: "/data/zxx/models/research/date/VOCdevkit/pascal_train.record" # 对应修改路径
   }
   label_map_path: "object_detection/data/pascal_label_map.pbtxt" # 对应修改路径
 }

 eval_config: {
   num_examples: 4952
 }

 eval_input_reader: {
   tf_record_input_reader {
     input_path: "/data/zxx/models/research/date/VOCdevkit/pascal_val.record" # 对应修改路径
   }
   label_map_path: "object_detection/data/pascal_label_map.pbtxt"  # 对应修改路径
   shuffle: false
   num_readers: 1
 }

修改好后，保存