How to reduce the number of training steps in Tensorflow’s Object Detection API?

I am following Dat Trans example to train my own Object Detector with TensorFlow’s Object Detector API.

I successfully started to train the custom objects. I am using CPU to train the model but it takes around 3 hour to complete 100 training steps. I suppose i have to change some parameter in .config.

I tried to convert .ckpt to .pb, I referred this post, but i was still not able to convert

1) How to reduce the number of training steps?
2) Is there a way to convert .ckpt to .pb.

Answer

I don’t think you can reduce the number of training step, but you can stop at any checkpoint(ckpt) and then convert it to .pb file
From TensorFlow Model git repository you can use , export_inference_graph.py
and following code

python tensorflow_models/object_detection/export_inference_graph.py 
--input_type image_tensor 
--pipeline_config_path architecture_used_while_training.config 
--trained path_to_saved_ckpt/model.ckpt-NUMBER 
--output_directory model/

where NUMBER refers to your latest saved checkpoint file number, however you can use older checkpoint file if you find it better in tensorboard