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
4 个月之前 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。
4 个月之前 回复
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For more information, please see: https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md https://github.com/tensorflow/addons If you depend on functionality not listed there, please file an issue. WARNING:tensorflow:Forced number of epochs for all eval validations to be 1. WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered eval_on_train_input_config.num_epochs = 0. Overwriting num_epochs to 1. WARNING:tensorflow:Estimator's model_fn (<function create_model_fn..model_fn at 0x0000027CBAB7BB70>) includes params argument, but params are not passed to Estimator. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\builders\dataset_builder.py:86: parallel_interleave (from tensorflow.contrib.data.python.ops.interleave_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.data.experimental.parallel_interleave(...). WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\core\preprocessor.py:196: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: seed2 arg is deprecated.Use sample_distorted_bounding_box_v2 instead. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\builders\dataset_builder.py:158: batch_and_drop_remainder (from tensorflow.contrib.data.python.ops.batching) is deprecated and will be removed in a future version. Instructions for updating: Use tf.data.Dataset.batch(..., drop_remainder=True). WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\tensorflow\python\ops\losses\losses_impl.py:448: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\tensorflow\python\ops\array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. 2019-08-14 16:29:31.607841: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7845 pciBusID: 0000:04:00.0 totalMemory: 6.00GiB freeMemory: 4.97GiB 2019-08-14 16:29:31.621836: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0 2019-08-14 16:29:32.275712: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: 2019-08-14 16:29:32.283072: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 2019-08-14 16:29:32.288675: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N 2019-08-14 16:29:32.293514: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4714 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:04:00.0, compute capability: 6.1) WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\eval_util.py:796: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\utils\visualization_utils.py:498: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version. Instructions for updating: tf.py_func is deprecated in TF V2. Instead, use tf.py_function, which takes a python function which manipulates tf eager tensors instead of numpy arrays. It's easy to convert a tf eager tensor to an ndarray (just call tensor.numpy()) but having access to eager tensors means tf.py_functions can use accelerators such as GPUs as well as being differentiable using a gradient tape. 2019-08-14 16:41:44.736212: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0 2019-08-14 16:41:44.741242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: 2019-08-14 16:41:44.747522: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 2019-08-14 16:41:44.751256: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N 2019-08-14 16:41:44.755548: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4714 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:04:00.0, compute capability: 6.1) WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\tensorflow\python\training\saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file APIs to check for files with this prefix. creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=2.43s). Accumulating evaluation results... DONE (t=0.14s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.287 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.529 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.278 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.162 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.061 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384 (default) D:\gitcode\models\research>
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ImportError:cannot import name 'cloud' from 'tensorflow.contrib'求助
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Eclpise进行mysql封装数组操作中对于筛选id大于1时,rs.next()一直为false,跪求各位大神
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关于object detection运行视频检测代码出现报错:ValueError:assignment destination is read-only
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在windows环境下使用自己的数据集跑faster rcnn遇到'NoneType' object is subscriptable问题(tensorflow)
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在jupyter notebook上运行tensorflow目标识别官方测试代码object_detection_tutorial.ipynb,每次都是最后一个模块运行时出现“服务器挂了”,如何解决?
在annaconda中创建了tensorflow-gpu的环境,代码可以跑通,没有报错,但是每次到最后一块检测test_image 的时候就服务器挂了。 创建tensorflowcpu环境可以正常跑下来(最后显示那个输出结果),请问是为什么?如何解决呢? 对该环境用代码测试过,pycharm里,可以显示应用的显卡信息,算力等信息,应该是没有问题的。
用TensorFlow 训练mask rcnn时,总是在执行训练语句时报错,进行不下去了,求大神
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Traceback (most recent call last): File "model_main.py", line 109, in <module> tf.app.run() File "E:\Python3.6\lib\site-packages\tensorflow\python\platform\app.py", line 126, in run _sys.exit(main(argv)) File "model_main.py", line 105, in main tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0]) File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\training.py", line 439, in train_and_evaluate executor.run() File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\training.py", line 518, in run self.run_local() File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\training.py", line 650, in run_local hooks=train_hooks) File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\estimator.py", line 363, in train loss = self._train_model(input_fn, hooks, saving_listeners) File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\estimator.py", line 843, in _train_model return self._train_model_default(input_fn, hooks, saving_listeners) File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\estimator.py", line 853, in _train_model_default input_fn, model_fn_lib.ModeKeys.TRAIN)) File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\estimator.py", line 691, in _get_features_and_labels_from_input_fn result = self._call_input_fn(input_fn, mode) File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\estimator.py", line 798, in _call_input_fn return input_fn(**kwargs) File "D:\Tensorflow\tf\models\research\object_detection\inputs.py", line 525, in _train_input_fn batch_size=params['batch_size'] if params else train_config.batch_size) File "D:\Tensorflow\tf\models\research\object_detection\builders\dataset_builder.py", line 149, in build dataset = data_map_fn(process_fn, num_parallel_calls=num_parallel_calls) File "E:\Python3.6\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py", line 853, in map return ParallelMapDataset(self, map_func, num_parallel_calls) File "E:\Python3.6\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py", line 1870, in __init__ super(ParallelMapDataset, self).__init__(input_dataset, map_func) File "E:\Python3.6\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py", line 1839, in __init__ self._map_func.add_to_graph(ops.get_default_graph()) File "E:\Python3.6\lib\site-packages\tensorflow\python\framework\function.py", line 484, in add_to_graph self._create_definition_if_needed() File "E:\Python3.6\lib\site-packages\tensorflow\python\framework\function.py", line 319, in _create_definition_if_needed self._create_definition_if_needed_impl() File "E:\Python3.6\lib\site-packages\tensorflow\python\framework\function.py", line 336, in _create_definition_if_needed_impl outputs = self._func(*inputs) File "E:\Python3.6\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py", line 1804, in tf_map_func ret = map_func(nested_args) File "D:\Tensorflow\tf\models\research\object_detection\builders\dataset_builder.py", line 130, in process_fn processed_tensors = transform_input_data_fn(processed_tensors) File "D:\Tensorflow\tf\models\research\object_detection\inputs.py", line 515, in transform_and_pad_input_data_fn tensor_dict=transform_data_fn(tensor_dict), File "D:\Tensorflow\tf\models\research\object_detection\inputs.py", line 129, in transform_input_data tf.expand_dims(tf.to_float(image), axis=0)) File "D:\Tensorflow\tf\models\research\object_detection\meta_architectures\faster_rcnn_meta_arch.py", line 543, in preprocess parallel_iterations=self._parallel_iterations) File "D:\Tensorflow\tf\models\research\object_detection\utils\shape_utils.py", line 237, in static_or_dynamic_map_fn outputs = [fn(arg) for arg in tf.unstack(elems)] File "D:\Tensorflow\tf\models\research\object_detection\utils\shape_utils.py", line 237, in <listcomp> outputs = [fn(arg) for arg in tf.unstack(elems)] File "D:\Tensorflow\tf\models\research\object_detection\core\preprocessor.py", line 2264, in resize_to_range lambda: _resize_portrait_image(image)) File "E:\Python3.6\lib\site-packages\tensorflow\python\util\deprecation.py", line 432, in new_func return func(*args, **kwargs) File "E:\Python3.6\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2063, in cond orig_res_t, res_t = context_t.BuildCondBranch(true_fn) File "E:\Python3.6\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 1913, in BuildCondBranch original_result = fn() File "D:\Tensorflow\tf\models\research\object_detection\core\preprocessor.py", line 2263, in <lambda> lambda: _resize_landscape_image(image), File "D:\Tensorflow\tf\models\research\object_detection\core\preprocessor.py", line 2245, in _resize_landscape_image align_corners=align_corners, preserve_aspect_ratio=True) TypeError: resize_images() got an unexpected keyword argument 'preserve_aspect_ratio' ``` 根据提示的最后一句,是说没有一个有效参数 我用的是TensorFlow1.8 python3.6,下载的最新的TensorFlow-models-master
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