# Preparing Inputs Tensorflow Object Detection API reads data using the TFRecord file format. Two sample scripts (`create_pascal_tf_record.py` and `create_pet_tf_record.py`) are provided to convert from the PASCAL VOC dataset and Oxford-IIIT Pet dataset to TFRecords. ## Generating the PASCAL VOC TFRecord files. The raw 2012 PASCAL VOC data set can be downloaded [here](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar), or by using the command below. Extract the tar file and run the `create_pascal_tf_record` script: ```bash # From tensorflow/models wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar tar -xvf VOCtrainval_11-May-2012.tar python object_detection/create_pascal_tf_record.py \ --label_map_path=object_detection/data/pascal_label_map.pbtxt \ --data_dir=VOCdevkit --year=VOC2012 --set=train \ --output_path=pascal_train.record python object_detection/create_pascal_tf_record.py \ --label_map_path=object_detection/data/pascal_label_map.pbtxt \ --data_dir=VOCdevkit --year=VOC2012 --set=val \ --output_path=pascal_val.record ``` You should end up with two TFRecord files named `pascal_train.record` and `pascal_val.record` in the `tensorflow/models` directory. ## Generating the Oxford-IIIT Pet TFRecord files. The Oxford-IIIT Pet data set can be downloaded from [their website](http://www.robots.ox.ac.uk/~vgg/data/pets/), or by using the command below. Extract the tar file and run the `create_pet_tf_record` script to generate TFRecords. ```bash # From tensorflow/models wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz tar -xvf annotations.tar.gz tar -xvf images.tar.gz python object_detection/create_pet_tf_record.py \ --label_map_path=object_detection/data/pet_label_map.pbtxt \ --data_dir=`pwd` \ --output_dir=`pwd` ``` You should end up with two TFRecord files named `pet_train.record` and `pet_val.record` in the `tensorflow/models` directory.