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  • Tensorflow学习笔记3: 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

      1 # Faster R-CNN with Resnet-101 (v1), configured for Pascal VOC Dataset.
      2 # Users should configure the fine_tune_checkpoint field in the train config as
      3 # well as the label_map_path and input_path fields in the train_input_reader and
      4 # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
      5 # should be configured.
      6 
      7 model {
      8   faster_rcnn {
      9     num_classes: 20 # 如果是自己数据集需要修改类目数
     10     image_resizer {
     11       keep_aspect_ratio_resizer {
     12         min_dimension: 600
     13         max_dimension: 1024
     14       }
     15     }
     16     feature_extractor {
     17       type: 'faster_rcnn_resnet101'
     18       first_stage_features_stride: 16
     19     }
     20     first_stage_anchor_generator {
     21       grid_anchor_generator {
     22         scales: [0.25, 0.5, 1.0, 2.0]
     23         aspect_ratios: [0.5, 1.0, 2.0]
     24         height_stride: 16
     25         width_stride: 16
     26       }
     27     }
     28     first_stage_box_predictor_conv_hyperparams {
     29       op: CONV
     30       regularizer {
     31         l2_regularizer {
     32           weight: 0.0
     33         }
     34       }
     35       initializer {
     36         truncated_normal_initializer {
     37           stddev: 0.01
     38         }
     39       }
     40     }
     41     first_stage_nms_score_threshold: 0.0
     42     first_stage_nms_iou_threshold: 0.7
     43     first_stage_max_proposals: 300
     44     first_stage_localization_loss_weight: 2.0
     45     first_stage_objectness_loss_weight: 1.0
     46     initial_crop_size: 14
     47     maxpool_kernel_size: 2
     48     maxpool_stride: 2
     49     second_stage_box_predictor {
     50       mask_rcnn_box_predictor {
     51         use_dropout: false
     52         dropout_keep_probability: 1.0
     53         fc_hyperparams {
     54           op: FC
     55           regularizer {
     56             l2_regularizer {
     57               weight: 0.0
     58             }
     59           }
     60           initializer {
     61             variance_scaling_initializer {
     62               factor: 1.0
     63               uniform: true
     64               mode: FAN_AVG
     65             }
     66           }
     67         }
     68       }
     69     }
     70     second_stage_post_processing {
     71       batch_non_max_suppression {
     72         score_threshold: 0.0
     73         iou_threshold: 0.6
     74         max_detections_per_class: 100
     75         max_total_detections: 300
     76       }
     77       score_converter: SOFTMAX
     78     }
     79     second_stage_localization_loss_weight: 2.0
     80     second_stage_classification_loss_weight: 1.0
     81   }
     82 }
     83 
     84 train_config: {
     85   batch_size: 1 # 每次喂的数据量
     86   optimizer {
     87     momentum_optimizer: {
     88       learning_rate: {
     89         manual_step_learning_rate {
     90           initial_learning_rate: 0.0001
     91           schedule {
     92             step: 500000
     93             learning_rate: .00001
     94           }
     95           schedule {
     96             step: 700000
     97             learning_rate: .000001
     98           }
     99         }
    100       }
    101       momentum_optimizer_value: 0.9
    102     }
    103     use_moving_average: false
    104   }
    105   gradient_clipping_by_norm: 10.0
    106   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
    107   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
    108   num_steps: 800000 # 最大训练次数
    109   data_augmentation_options {
    110     random_horizontal_flip {
    111     }
    112   }
    113 }
    114 
    115 train_input_reader: {
    116   tf_record_input_reader {
    117     input_path: "/data/zxx/models/research/date/VOCdevkit/pascal_train.record" # 对应修改路径
    118   }
    119   label_map_path: "object_detection/data/pascal_label_map.pbtxt" # 对应修改路径
    120 }
    121 
    122 eval_config: {
    123   num_examples: 4952
    124 }
    125 
    126 eval_input_reader: {
    127   tf_record_input_reader {
    128     input_path: "/data/zxx/models/research/date/VOCdevkit/pascal_val.record" # 对应修改路径
    129   }
    130   label_map_path: "object_detection/data/pascal_label_map.pbtxt"  # 对应修改路径
    131   shuffle: false
    132   num_readers: 1
    133 }

    修改好后,保存

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  • 原文地址:https://www.cnblogs.com/zheng1076/p/10334586.html
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