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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-IIT Pet dataset to TFRecords.

Generating the PASCAL VOC TFRecord files.

The raw 2012 PASCAL VOC data set can be downloaded here. Extract the tar file and run the create_pascal_tf_record script:

# From tensorflow/models/object_detection
tar -xvf VOCtrainval_11-May-2012.tar
./create_pascal_tf_record --data_dir=VOCdevkit \
    --year=VOC2012 --set=train --output_path=pascal_train.record
./create_pascal_tf_record --data_dir=/home/user/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/object_detection directory.

The label map for the PASCAL VOC data set can be found at data/pascal_label_map.pbtxt.

Generation the Oxford-IIT Pet TFRecord files.

The Oxford-IIT Pet data set can be downloaded from their website. Extract the tar file and run the create_pet_tf_record script to generate TFRecords.

# From tensorflow/models/object_detection
tar -xvf annotations.tar.gz
tar -xvf images.tar.gz
./create_pet_tf_record --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/object_detection directory.

The label map for the Pet dataset can be found at data/pet_label_map.pbtxt.