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dataset_conversion.md

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Dataset download and conversion

This file contains instructions to download the individual datasets used by Meta-Dataset, and convert them into a common format (one TFRecord file per class). See an overview for more context.

ilsvrc_2012

  1. Download ilsvrc2012_img_train.tar, from the ILSVRC2012 website

  2. Extract it into ILSVRC2012_img_train/, which should contain 1000 files, named n????????.tar (expected time: ~30 minutes)

  3. Extract each of ILSVRC2012_img_train/n????????.tar in its own directory (expected time: ~30 minutes), for instance:

    for FILE in *.tar;
do
  mkdir ${FILE/.tar/};
  cd ${FILE/.tar/};
  tar xvf ../$FILE;
  cd ..;
done

  4. Download the following two files into ILSVRC2012_img_train/:

  5. Launch the conversion script:

    python -m meta_dataset.dataset_conversion.convert_datasets_to_records \
  --dataset=ilsvrc_2012 \
  --ilsvrc_2012_data_root=$DATASRC/ILSVRC2012_img_train \
  --splits_root=$SPLITS \
  --records_root=$RECORDS

  6. Expect the conversion to take 4 to 12 hours, depending on the filesystem's latency and bandwidth.

  7. Find the following outputs in $RECORDS/ilsvrc_2012/:

    • 1000 tfrecords files named [0-999].tfrecords
    • dataset_spec.json (see note 1)
    • num_leaf_images.json

omniglot

  1. Download images_background.zip and images_evaluation.zip

  2. Extract them into the same omniglot/ directory

  3. Launch the conversion script:

    python -m meta_dataset.dataset_conversion.convert_datasets_to_records \
  --dataset=omniglot \
  --omniglot_data_root=$DATASRC/omniglot \
  --splits_root=$SPLITS \
  --records_root=$RECORDS

  4. Expect the conversion to take a few seconds.

  5. Find the following outputs in $RECORDS/omniglot/:

    • 1623 tfrecords files named [0-1622].tfrecords
    • dataset_spec.json (see note 1)

aircraft

  1. Download fgvc-aircraft-2013b.tar.gz

  2. Extract it into fgvc-aircraft-2013b

  3. Launch the conversion script:

    python -m meta_dataset.dataset_conversion.convert_datasets_to_records \
  --dataset=aircraft \
  --aircraft_data_root=$DATASRC/fgvc-aircraft-2013b \
  --splits_root=$SPLITS \
  --records_root=$RECORDS

  4. Expect the conversion to take 5 to 10 minutes.

  5. Find the following outputs in $RECORDS/aircraft/:

    • 100 tfrecords files named [0-99].tfrecords
    • dataset_spec.json (see note 1)

cu_birds

  1. Download CUB_200_2011.tgz

  2. Extract it into CUB_200_2011/ (and attributes.txt)

  3. Launch the conversion script:

    python -m meta_dataset.dataset_conversion.convert_datasets_to_records \
  --dataset=cu_birds \
  --cu_birds_data_root=$DATASRC/CUB_200_2011 \
  --splits_root=$SPLITS \
  --records_root=$RECORDS

  4. Expect the conversion to take around one minute.

  5. Find the following outputs in $RECORDS/cu_birds/:

    • 200 tfrecords files named [0-199].tfrecords
    • dataset_spec.json (see note 1)

dtd

  1. Download dtd-r1.0.1.tar.gz

  2. Extract it into dtd/

  3. Launch the conversion script:

    python -m meta_dataset.dataset_conversion.convert_datasets_to_records \
  --dataset=dtd \
  --dtd_data_root=$DATASRC/dtd \
  --splits_root=$SPLITS \
  --records_root=$RECORDS

  4. Expect the conversion to take a few seconds.

  5. Find the following outputs in $RECORDS/dtd/:

    • 47 tfrecords files named [0-46].tfrecords
    • dataset_spec.json (see note 1)

quickdraw

  1. Download all 345 .npy files hosted on Google Cloud

    • You can use gsutil to download them to quickdraw/:

      gsutil -m cp gs://quickdraw_dataset/full/numpy_bitmap/*.npy $DATASRC/quickdraw

  2. Launch the conversion script:

    python -m meta_dataset.dataset_conversion.convert_datasets_to_records \
  --dataset=quickdraw \
  --quickdraw_data_root=$DATASRC/quickdraw \
  --splits_root=$SPLITS \
  --records_root=$RECORDS

  3. Expect the conversion to take 3 to 4 hours.

  4. Find the following outputs in $RECORDS/quickdraw/:

    • 345 tfrecords files named [0-344].tfrecords
    • dataset_spec.json (see note 1)

fungi

  1. Download fungi_train_val.tgz and train_val_annotations.tgz

  2. Extract them into the same fungi/ directory. It should contain one images/ directory, as well as train.json and val.json.

  3. Launch the conversion script:

    python -m meta_dataset.dataset_conversion.convert_datasets_to_records \
  --dataset=fungi \
  --fungi_data_root=$DATASRC/fungi \
  --splits_root=$SPLITS \
  --records_root=$RECORDS

  4. Expect the conversion to take 5 to 15 minutes.

  5. Find the following outputs in $RECORDS/fungi/:

    • 1394 tfrecords files named [0-1393].tfrecords
    • dataset_spec.json (see note 1)

vgg_flower

  1. Download 102flowers.tgz and imagelabels.mat

  2. Extract 102flowers.tgz, it will create a jpg/ sub-directory

  3. Launch the conversion script:

    python -m meta_dataset.dataset_conversion.convert_datasets_to_records \
  --dataset=vgg_flower \
  --vgg_flower_data_root=$DATASRC/vgg_flower \
  --splits_root=$SPLITS \
  --records_root=$RECORDS

  4. Expect the conversion to take about one minute.

  5. Find the following outputs in $RECORDS/vgg_flower/:

    • 102 tfrecords files named [0-101].tfrecords
    • dataset_spec.json (see note 1)

traffic_sign

  1. Download GTSRB_Final_Training_Images.zip If the link happens to be broken, browse the GTSRB dataset website for more information.

  2. Extract it in $DATASRC, it will create a GTSRB/ sub-directory

  3. Launch the conversion script:

    python -m meta_dataset.dataset_conversion.convert_datasets_to_records \
  --dataset=traffic_sign \
  --traffic_sign_data_root=$DATASRC/GTSRB \
  --splits_root=$SPLITS \
  --records_root=$RECORDS

  4. Expect the conversion to take about one minute.

  5. Find the following outputs in $RECORDS/traffic_sign/:

    • 43 tfrecords files named [0-42].tfrecords
    • dataset_spec.json (see note 1)

mscoco

  1. Download the 2017 train images and annotations from http://cocodataset.org/:

    • You can use gsutil to download them to mscoco/:

      cd $DATASRC/mscoco/ mkdir train2017
gsutil -m rsync gs://images.cocodataset.org/train2017 train2017
gsutil -m cp gs://images.cocodataset.org/annotations/annotations_trainval2017.zip
unzip annotations_trainval2017.zip

    • Otherwise, you can download train2017.zip and annotations_trainval2017.zip and extract them into mscoco/.

  2. Launch the conversion script:

    python -m meta_dataset.dataset_conversion.convert_datasets_to_records \
  --dataset=mscoco \
  --mscoco_data_root=$DATASRC/mscoco \
  --splits_root=$SPLITS \
  --records_root=$RECORDS

  3. Expect the conversion to take about 4 hours.

  4. Find the following outputs in $RECORDS/mscoco/:

    • 80 tfrecords files named [0-79].tfrecords
    • dataset_spec.json (see note 1)

Notes

  1. A reference version of each of the dataset_spec.json files is part of this repository. You can compare them with the version generated by the conversion process for troubleshooting.