brillant-ordinary

1.训练文件的配置

将生成的csv和record文件都放在新建的mydata文件夹下,并打开object_detection文件夹下的data文件夹,复制一个后缀为.pbtxt的文件到mtdata文件夹下,并重命名为gaoyue.pbtxt

用记事本打开该文件,因为我只分了一类,所以将其他内容删除,只剩下这一个类别,并将name改为gaoyue。

这时我们拥有的所有文件如下图所示。

我们在object_detection文件夹下新建一个training文件夹,在里面新建一个记事本文件并命名为 ssd_mobilenet_v1_coco.config

打开,输入以下代码,按右边注释进行修改

# SSD with Mobilenet v1 configuration for MSCOCO 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                          # 你类别的数量,我这里只分了一类
    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: 16                                       # 电脑好的话可以调高点,我电脑比较渣就调成16了
  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
    }
  }

  # 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                   # 训练的steps
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    ssd_random_crop {
    }
  }
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "mydata/gaoyue_train.record"                 # 训练的tfrrecord文件路径
  }
  label_map_path: "mydata/gaoyue.pbtxt"
}

eval_config: {
  num_examples: 8000             # 验证集的数量
  # Note: The below line limits the evaluation process to 10 evaluations.
  # Remove the below line to evaluate indefinitely.
  max_evals: 10
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "mydata/gaoyue_test.record"                   # 验证的tfrrecord文件路径
  }
  label_map_path: "mydata/gaoyue.pbtxt"
  shuffle: false
  num_readers: 1
}

新建后的文件显示如下。

 这时,我们训练的准备工作就做好了。

2.训练模型

在object_detection文件夹下打开Anaconda Prompt,输入命令

python model_main.py --pipeline_config_path=training/ssd_mobilenet_v1_coco.config --model_dir=training  --alsologtostderr

 

在训练过程中如果出现no model named pycocotools的问题的话,请参考这个网址(http://www.mamicode.com/info-detail-2660241.html)解决。亲测有效

即:

(1)从https://github.com/pdollar/coco.git 下载源码,解压至全英文路径下。

(2)使用cmd进入解压后的cocoapi-master/PythonAPI路径下,输入python setup.py build_ext --inplace。如果这一步有报错,请打开set_up.py文件,将其中这两个参数删除。

即:

 

(3)上一步执行没问题之后,继续在cmd窗口运行命令:python setup.py build_ext install

 

训练完成后,training文件夹下是这样的情况

 

 

(如果想观察训练过程中参数的变化以及网络的话,可以打开新的一个Anaconda Prompt cd到object_detection文件夹下

输入命令:tensorboard --logdir=training),复制出现的网址即可。如图所示\

如果显示不出来的话,新建网页在地址栏输入http://localhost:6006/(后面的6006是我的端口号,根据你自己的输入)

3.生成模型

定位到object_detection目录下,打开Anaconda Promp输入命令

python export_inference_graph.py \ --input_type image_tensor \ --pipeline_config_path training/ssd_mobilenet_v1_coco.config \ --trained_checkpoint_prefix training/model.ckpt-500 \ --output_directory gaoyue_detection

(注意这两处标红的地方,1.    model.ckpt-500是指你训练的轮数的文件,这里因为我只训练了500轮,所以改成了500(如下图中的500

2.   output_directory是输出模型的路径,最好是新建一个文件夹来存放模型,我新建了一个名为gaoyue_detection的模型)

命令执行完成后,打开gaoyue_detection文件夹,里面的内容如图所示

表示执行成功,这样,我们用自己数据集训练的目标检测模型就做好了

下一节会详细说我们自己模型的验证

 

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