Tensorflow 对象检测下一步 [英] Tensorflow object detection next steps

查看：66 发布时间：2021/6/11 19:50:35 python tensorflow object-detection object-detection-api

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问题描述

我正在尝试训练一个模型来检查图像、识别指定的对象并告诉我它的坐标(我什至不需要看到对象周围的正方形).

Im trying to train a model to check images, identify specified objects and tell me its coodinates (i dont even need to see an square around the object).

为此，我使用 Tensorflow 的对象检测，而我所做的大部分工作都是在查看本教程:

For this im using Tensorflow's object detection and most of what I did was looking this tutorial:

如何训练对象在 Windows 10 上使用 TensorFlow (GPU) 的多个对象检测分类器

但是有些事情发生了变化，可能是因为更新，然后我不得不自己做一些事情.我实际上可以训练模型(我猜)，但我不明白评估结果.我曾经看到损失和当前步骤，但这个输出对我来说是不寻常的.此外，我认为培训并没有得到保存.

But some things changed, probably because of updates, and then I had to do somethings on my own. I can actually train the model (I guess) but I don't understand the evaluation results. Im used to see loss and current step but this output is unusual for me. Also I don't think the training is being saved.

model_main.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_coco.config

配置文件:

model {
  faster_rcnn {
    num_classes: 9
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 600
        max_dimension: 1024
      }
    }
    feature_extractor {
      type: 'faster_rcnn_inception_v2'
      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: 5
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        manual_step_learning_rate {
          initial_learning_rate: 0.0002
          schedule {
            step: 900000
            learning_rate: .00002
          }
          schedule {
            step: 1200000
            learning_rate: .000002
          }
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  gradient_clipping_by_norm: 10.0
  fine_tune_checkpoint: "faster_rcnn_inception_v2_coco_2018_01_28/model.ckpt"
  from_detection_checkpoint: true
  num_steps: 50000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "C:/tensorflow1/models/research/object_detection/images/train.record"
  }
  label_map_path: "C:/tensorflow1/models/research/object_detection/training/object-detection.pbtxt"
}

eval_config: {
  num_examples: 67
  max_evals: 10
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "C:/tensorflow1/models/research/object_detection/images/test.record"
  }
  label_map_path: "C:/tensorflow1/models/research/object_detection/training/object-detection.pbtxt"
  shuffle: false
  num_readers: 1
}

输出:

2019-03-16 01:05:23.842424: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-03-16 01:05:23.842528: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-03-16 01:05:23.845561: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2019-03-16 01:05:23.845777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2019-03-16 01:05:23.847854: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6390 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)
creating index...
index created!
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.05s).
Accumulating evaluation results...
DONE (t=0.04s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.681
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.670
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.542
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.825
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.682
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.689
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.689
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.556
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.825
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000

还有 faster_rcnn_inception_v2_coco_2018_01_28 里面的模型自 2018 年 1 月以来就没有改变，这可能意味着即使是训练，也没有保存进度.

Also the models inside faster_rcnn_inception_v2_coco_2018_01_28 have not been changed since Jan 2018, which probably means that even if it's training, it's not saving the progress.

我的问题是:

是我的配置有问题还是其他什么问题?
是否保存了训练进度?
我如何理解这个输出?(IoU?maxDets?区域?负精确?是单批还是什么?)
我是否应该等待它最终自行停止?我看不到我在哪一步，只是我用作的这段输出这里的示例用了将近 15 分钟才出现.

Tensorflow 对象检测下一步 [英] Tensorflow object detection next steps

问题描述

配置文件:

输出:

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Tensorflow 对象检测下一步 [英] Tensorflow object detection next steps

问题描述

配置文件:

输出:

推荐答案

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Python最新文章

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