Tensorflow object detection api 训练自己数据 map一直是 -1 300C

使用Tensorflow object detection api 训练自己的数据 map 一直是-1.loss一直也很低。
结果是这样的:
图片说明

loss:

图片说明

使用的模型是:model zoo的这个图片说明

piplineConfig 如下:

model {
  faster_rcnn {
    num_classes: 25
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 720
        max_dimension: 1280
      }
    }
    feature_extractor {
      type: "faster_rcnn_resnet50"
      first_stage_features_stride: 16
    }
    first_stage_anchor_generator {
      grid_anchor_generator {
        height_stride: 16
        width_stride: 16
        scales: 0.25
        scales: 0.5
        scales: 1.0
        scales: 2.0
        aspect_ratios: 0.5
        aspect_ratios: 1.0
        aspect_ratios: 2.0
      }
    }
    first_stage_box_predictor_conv_hyperparams {
      op: CONV
      regularizer {
        l2_regularizer {
          weight: 0.0
        }
      }
      initializer {
        truncated_normal_initializer {
          stddev: 0.00999999977648
        }
      }
    }
    first_stage_nms_score_threshold: 0.0
    first_stage_nms_iou_threshold: 0.699999988079
    first_stage_max_proposals: 100
    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 {
        fc_hyperparams {
          op: FC
          regularizer {
            l2_regularizer {
              weight: 0.0
            }
          }
          initializer {
            variance_scaling_initializer {
              factor: 1.0
              uniform: true
              mode: FAN_AVG
            }
          }
        }
        use_dropout: false
        dropout_keep_probability: 1.0
      }
    }
    second_stage_post_processing {
      batch_non_max_suppression {
        score_threshold: 0.300000011921
        iou_threshold: 0.600000023842
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SOFTMAX
    }
    second_stage_localization_loss_weight: 2.0
    second_stage_classification_loss_weight: 1.0
  }
}
train_config {
  batch_size: 1
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  optimizer {
    momentum_optimizer {
      learning_rate {
        manual_step_learning_rate {
          initial_learning_rate: 0.000300000014249
          schedule {
            step: 900000
            learning_rate: 2.99999992421e-05
          }
          schedule {
            step: 1200000
            learning_rate: 3.00000010611e-06
          }
        }
      }
      momentum_optimizer_value: 0.899999976158
    }
    use_moving_average: false
  }
  gradient_clipping_by_norm: 10.0
  fine_tune_checkpoint: "/home/yons/code/自动驾驶视觉综合感知/faster_rcnn_resnet50_coco_2018_01_28/model.ckpt"
  from_detection_checkpoint: true
  num_steps: 200000
}
train_input_reader {
  label_map_path: "/home/yons/code/自动驾驶视觉综合感知/pascal_label_map.pbtxt"
  tf_record_input_reader {
    input_path: "/home/yons/data/自动驾驶视觉综合感知/train_dataset/tfRecord/train/coco_train.record"
  }
}
eval_config {
  num_examples: 200
  max_evals: 10
  use_moving_averages: false
  metrics_set: "coco_detection_metrics"
}
eval_input_reader {
  label_map_path: "/home/yons/code/自动驾驶视觉综合感知/pascal_label_map.pbtxt"
  shuffle: false
  num_readers: 1
  tf_record_input_reader {
    input_path: "/home/yons/data/自动驾驶视觉综合感知/train_dataset/tfRecord/val/coco_val.record"
  }
}

label_map配置:

item {
  id: 1
  name: 'red'
}


item {
  id: 2
  name: 'green'
}


item {
  id: 3
  name: 'yellow'
}

item {
  id: 4
  name: 'red_left'
}

item {
  id: 5
  name: 'red_right'
}

item {
  id: 6
  name: 'yellow_left'
}

item {
  id: 7
  name: 'yellow_right'
}

item {
  id: 8
  name: 'green_left'
}

item {
  id: 9
  name: 'green_right'
}


item {
  id: 10
  name: 'red_forward'
}

item {
  id: 11
  name: 'green_forward'
}

item {
  id: 12
  name: 'yellow_forward'
}

item {
  id:13
  name: 'horizon_red'
}


item {
  id: 14
  name: 'horizon_green'
}

item {
  id: 15
  name: 'horizon_yellow'
}

item {
  id: 16
  name: 'off'
}

item {
  id: 17
  name: 'traffic_sign'
}


item {
  id: 18
  name: 'car'
}


item {
  id: 19
  name: 'motor'
}

item {
  id: 20
  name: 'bike'
}

item {
  id: 21
  name: 'bus'
}

item {
  id: 22
  name: 'truck'
}

item {
  id: 23
  name: 'suv'
}

item {
  id: 24
  name: 'express'
}

item {
  id: 25
  name: 'person'
}

自己解析数据tfrecord:

图片说明

图片说明

2个回答

样本太少,训练时间过短。这种模型需要非常多的图片才能学出来

weixin_29936421
weixin_29936421 感谢回答,我有训练时间长的,这个截图是刚训练时截取的。忘了贴出来loss 。loss一开始就不正常。都非常底
大约一年之前 回复
weixin_29936421
weixin_29936421 我把我的数据集先转成coco格式,然后使用models的转化工具(create_coco_tf_record.py) 转化成 tfrecord的。其他的和这个博客没啥变化了。
大约一年之前 回复
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