Exact settings to train the provided SSD models on COCO dataset

System information

• What is the top-level directory of the model you are using:
  tensorflow/models/research/object_detection
• Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
  No
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
  Ubuntu 18.04
• TensorFlow installed from (source or binary):
  From source
• TensorFlow version (use command below):
  1.9.0
• Bazel version (if compiling from source):
  0.15
• CUDA/cuDNN version:
  9.2/7.1
• GPU model and memory:
  GTX Geforce 1070
• Exact command to reproduce:

Describe the problem

I was wondering if there is any document, explanation, anything! to guide through training the provided ssd configs on COCO dataset. I believe that the provided config files are configured for fine-tuning the pre-trained weights on COCO on another dataset. But, I want to know how to fine-tune a SSD model, say ssd_inception_v2, from a pre-trained model on ImageNet on COCO dataset. I have plenty of experience in fine-tuning a COCO pre-train model on another dataset (UA-DETRAC) and they all have been successful, I have also fine-tuned models trained on ImageNet on the same dataset and these kinds of models also work. However, I'm not able to train the provided configs on COCO dataset to create the reported results. I have examined with quite a number of training hyper-parameters and all of them have failed (by failing I mean getting an AP of 0!).