Tensorflow Object Detection API - TFRecord creation

Understand how to create TFRecords is an important point when you use Tensorflow Object Detection API. In my Github repo (https://github.com/koheikawata/objectdetectiontest), you can find what directory structure looks like.

``` . └── research ├── object_detection | ├── legacy | | ├── train.py | | └── eval.py | └── test1 | ├── eval_images | | └── export-image-0-b'IMG_0000'.png | ├── checkpoints | | └── model.ckpt-4495.data-00000-of-00001 | | └── model.ckpt-4495.index | | └── model.ckpt-4495.meta | ├── output | | └── saved_model | | └── frozen_inference_graph.pb | ├── Annotations | | └── ***.xml | ├── JPEGImages | | └── ***.jpg | ├── flask_pics | ├── pascal_label_map.pbtxt | ├── train.record | └── val.record ├── faster_rcnn_resnet101_coco_2018_01_28 | └── model.ckpt ├── faster_rcnn_resnet101_coco_test1.config ├── create_tf_record_test1.py ├── object_detection_app_test1.py ├── object_detection_inference_test1.py ├── object_detection_app_test1_docker.py └── Dockerfile ```