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As mentioned in my other thread (Tensorflow Object Detection - Avoid overlapping boxes) I'm new to machine learning and I have to implement an algorithm for detecting traffic lights.

Regarding Tensorflow and it's possibilities, I've got a whole bunch of questions I don't know where to ask except Stack Overflow.

  • For a quick start I've downloaded a pre trained model and started training it using the Bosch Traffic Lights Dataset. Using a pre trained model is fine but every now and then I wonder if it's possible to modify this model (add or remove a layer) or if it would be best to use keras on top of tf for better customization possibilities.

  • Additionally I wonder how I should know what are the possible configurations in the pipeline.config file inside every pre trained model. Is there any documentation or do I have to dig into the python files to get into it? In other words, does it even make any sense to change the configuration?

  • For documentation purpose we're using tensorboard. Unfortunately there's no accuracy (but loss) documented out of the box - how do we get the accuracy displayed as an additional graph?

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  • Please prefer to ask only one question per post, that makes it easier to search and answer the questions. Commented Feb 5, 2019 at 17:09

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You should use configuration to tune all the aspects. As mentioned in Tensorflow object detection config files documentation, configuration parameters can be browser in the protocol buffers message definitions. For example, for the model, if you are using faster RCNN, have a look at the different fields of the FasterRcnn message. You could export a trained model, load it in a regular TensorFlow script and add anything you want to it for whatever purpose, but the object detection framework is meant to be configuration-driven.

For the metrics, have a look at Supported object detection evaluation protocols. In the EvalConfig message, there is a metrics_set that you can set to different values for different evaluation metrics.

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