Reduce Training steps for SSD-300

I am new to deep learning and I am trying to train my SSD-300 (single shot detector) model which is taking too long. For example even though I ran 50 epochs, it is training for 108370+ global steps. I am using the default train_ssd_network.py file from the official github repo: https://github.com/balancap/SSD-Tensorflow

The command I ran for training:

!python train_ssd_network.py --dataset_name=pascalvoc_2007 epochs= 50 --dataset_split_name=train --model_name=ssd_300_vgg --save_summaries_secs=60 --save_interval_secs=600 --weight_decay=0.0005 --optimizer=adam --learning_rate=0.001 --batch_size=6 --gpu_memory_fraction=0.9 --checkpoint_exclude_scopes =ssd_300_vgg/conv6,ssd_300_vgg/conv7,ssd_300_vgg/block8,ssd_300_vgg/block9,ssd_300_vgg/block10,ssd_300_vgg/block11,ssd_300_vgg/block4_box,ssd_300_vgg/block7_box,ssd_300_vgg/block8_box,ssd_300_vgg/block9_box,ssd_300_vgg/block10_box,ssd_300_vgg/block11_box

How can I change the training steps and what is the ideal training steps?