blocks|key|2324760|text|您应该参考教程页面上的在本地运行部分。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2324761|下面是示例配置：|2324762|#From+the+tensorflow/models/research/+directory
PIPELINE_CONFIG_PATH={path+to+pipeline+config+file}
MODEL_DIR={path+to+model+directory}
NUM_TRAIN_STEPS=50000
NUM_EVAL_STEPS=2000
python+object_detection/model_main.py+\
++++--pipeline_config_path=${PIPELINE_CONFIG_PATH}+\
++++--model_dir=${MODEL_DIR}+\
++++--num_train_steps=${NUM_TRAIN_STEPS}+\
++++--num_eval_steps=${NUM_EVAL_STEPS}+\
++++--alsologtostderr+|code-block|syntax|javascript|2324763|和运行张紧板：|2324764|tensorboard+--logdir=${MODEL_DIR}|2324765|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_locally.md#running-locally^0|B|5|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@$A|X|B|Y|1|Z]]|C|$]]|$1|D|3|E|5|6|7|10|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|11|8|@]|9|@]|C|$I|J]]|$1|K|3|L|5|6|7|12|8|@]|9|@]|C|$]]|$1|M|3|N|5|H|7|13|8|@]|9|@]|C|$I|J]]|$1|O|3|-4|5|6|7|14|8|@]|9|@]|C|$]]]|P|$Q|$5|R|S|T|C|$U|V]]]]

You should refer to <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_locally.md#running-locally" rel="noreferrer">Running locally</a>
 section on the tutorial page.

Here is the sample configuration:

<pre><code>#From the tensorflow/models/research/ directory
PIPELINE_CONFIG_PATH={path to pipeline config file}
MODEL_DIR={path to model directory}
NUM_TRAIN_STEPS=50000
NUM_EVAL_STEPS=2000
python object_detection/model_main.py \
 --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
 --model_dir=${MODEL_DIR} \
 --num_train_steps=${NUM_TRAIN_STEPS} \
 --num_eval_steps=${NUM_EVAL_STEPS} \
 --alsologtostderr 
</code></pre>

and to run tensorboard: 

<pre><code>tensorboard --logdir=${MODEL_DIR}
</code></pre>

blocks|key|248046|text|您可以使用遗赠培训和评估脚本，但我们建议使用model_main。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|248047|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/tensorflow/models/tree/master/research/object_detection/legacy^0|5|2|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

You can use the <a href="https://github.com/tensorflow/models/tree/master/research/object_detection/legacy" rel="nofollow noreferrer">legacy</a> train and evaluation scripts, but we recommend using model_main.

blocks|key|1092222|text|为了做出一些澄清，正如德里克·周所提到的，火车和评估python脚本最近(大约6天前)被移到了“遗留”目录中。假设你想继续用旧的方式..。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1092223|如果一个人从打电话开始训练：|1092224|python+train.py+--logtostderr+--train_dir=training/+--pipeline_config_path=training/ssd_mobilenet_v1_pets.config|code-block|syntax|javascript|1092225|人们会知道，通过呼叫开始训练：|1092226|python+legacy/train.py+--logtostderr+--train_dir=training/+--pipeline_config_path=training/ssd_mobilenet_v1_pets.config|1092227|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|Q|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|R|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|S|8|@]|9|@]|A|$G|H]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

For some clarification, as aforementioned by Derek Chow, it seems the train and evaluation python scripts were recently (~6 days ago) moved into the 'legacy' directory. 
Assuming you wanted to continue using the old way..

If one was beginning training by calling:

<pre><code>python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config
</code></pre>

One would know begin training by calling:

<pre><code>python legacy/train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config
</code></pre>

blocks|key|2324844|text|在最新的合并中，列车和eval移动到了遗留的dir。如果您使用教程，可以转到以前的版本。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2324845|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

in the newest merge the train and eval moved to legacy dir. You can go to a previous version if you work with a tutorial.

I've correctly installed Tensorflow Object Detection API according to the provided documentation. However, when I need to train my network there is no train.py file in the research/object_detection directory.
Is there anything I can do to fix this?

Link: <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md" rel="noreferrer">https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md</a>

Tensorflow Object Detection API no train.py file

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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根据提供的文档，我正确地安装了Tensorflow对象检测API。然而，当我需要训练我的网络时，搜索/对象检测目录中没有train.py文件。我能做些什么来解决这个问题吗？链接：

问Tensorflow对象检测API无train.py文件
EN

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问Tensorflow对象检测API无train.py文件EN