cvdatasets/scripts/info_files/info.yml

BASE_DIR: /home/korsch_data/

DATA_DIR: datasets
MODEL_DIR: models

############ Existing models
### weights are used for fine-tuning
MODELS:
  efficientnet:    &efficientnet
    folder: efficientnet
    class_key: efficientnet
    weights: model.imagenet.npz

  inception_inat:    &inception_inat
    folder: inception
    class_key: inception
    weights: model.inat.ckpt.npz

  inception_imagenet:    &inception_inet
    folder: inception
    class_key: inception
    weights: model.imagenet.ckpt.npz

  inception:    &inception
    <<: *inception_inat

  inception_tf_inat:  &inception_tf_inat
    folder: inception_tf
    class_key: inception_tf
    weights: inception_v3_iNat_299.ckpt

  inception_tf_inet:  &inception_tf_inet
    folder: inception_tf
    class_key: inception_tf
    weights: inception_v3_ILSVRC_299.ckpt

  inception_tf:  &inception_tf
    <<: *inception_tf_inat

  resnet:       &resnet50
    folder: resnet
    class_key: resnet
    weights: model.npz

  vgg19:       &vgg19
    folder: vgg19
    class_key: vgg19
    weights: model.npz

############ Existing Datasets
DATASETS:

  IMAGENET:         &inet
    folder: ImageNet
    annotations: "BJOERN"
    annotation_type: FOLDER
    train_images: ILSVRC2012_img_train
    val_images: ILSVRC2012_img_val
    n_classes: 1000

  IMAGENET_TOP_INAT20: &inet_top_inat20
    <<: *inet
    annotations: "TOP_INAT20"
    n_classes: 44

  CUB200:         &cub200
    folder: birds/cub200
    annotations: "ORIGINAL"
    annotation_type: FILE_LIST
    n_classes: 200

  CUB10:         &cub10
    <<: *cub200
    annotations: "cub10"
    n_classes: 10

  CUB200_2FOLD:   &cub200_2fold
    <<: *cub200
    folder: birds/cub200_2fold

  CUB200_GOOGLE: &cub200_google
    <<: *cub200
    folder: birds/cub200_google_images

  CUB200_GOOGLE_SEM: &cub200_google_sem
    <<: *cub200
    folder: birds/cub200_google_images
    annotations: "sem_selected"

  NAB:         &nabirds
    folder: birds/nabirds
    annotations: "ORIGINAL"
    annotation_type: FILE_LIST
    n_classes: 555

  BIRDSNAP:         &birdsnap
    folder: birds/birdsnap
    annotations: "RESIZED"
    annotation_type: FILE_LIST
    n_classes: 500

  CARS:         &cars
    folder: cars
    annotations: "ORIGINAL"
    annotation_type: FILE_LIST
    n_classes: 196

  DOGS:         &dogs
    folder: dogs
    annotations: "ORIGINAL"
    annotation_type: FILE_LIST
    n_classes: 120

  FLOWERS:         &flowers
    folder: flowers
    annotations: "flowers102"
    annotation_type: FILE_LIST
    n_classes: 102

  HERBA19:         &herba19
    folder: herbarium
    annotations: "2019"
    annotation_type: FOLDER
    train_images: small-train
    val_images: small-validation
    test_images: small-test
    n_classes: 683

  INAT20:         &inat20
    folder: inat
    annotations: "2020/PLAIN"
    annotation_type: JSON
    n_classes: 200

  INAT20_IN_CLASS:         &inat20_in_class
    <<: *inat20
    annotations: "2020/IN_CLASS"

  INAT20_NOISY_IN_CLASS: &inat20_noisy_in_class
    <<: *inat20
    annotations: "2020/NOISY_IN_CLASS"

  INAT20_OUT_CLASS:         &inat20_out_class
    <<: *inat20
    annotations: "2020/OUT_CLASS"

  INAT20_NOISY_OUT_CLASS:         &inat20_noisy_out_class
    <<: *inat20
    annotations: "2020/NOISY_OUT_CLASS"

  INAT20_U_OUT_CLASS:         &inat20_u_out_class
    <<: *inat20
    annotations: "2020/U_OUT_CLASS"

  INAT20_TEST:    &inat20_test
    <<: *inat20
    annotations: "2020/TEST"

  INAT19:         &inat19
    folder: inat
    annotations: "2019"
    annotation_type: JSON
    n_classes: 1010

  INAT19_MINI:    &inat19_mini
    <<: *inat19
    annotations: "2019_small"
    feature_suffix: .mini

  INAT19_TEST:    &inat19_test
    <<: *inat19
    annotations: "2019_test"
    feature_suffix: .test

  INAT18:         &inat18
    folder: inat
    annotations: "2018"
    annotation_type: JSON
    n_classes: 8142

  HED:         &hed
    folder: medical/HED
    annotations: "patches224x224"
    annotation_type: FILE_LIST
    n_classes: 2

  TIGERS:         &tigers
    folder: tigers
    annotations: "reid/train"
    annotation_type: FILE_LIST
    n_classes: 107

  TIGERS_TEST:         &tigers_test
    <<: *tigers
    annotations: "reid/test"

############ Existing Part Annotations and Part Features
### feature file name composition:
# ${BASE_DIR}/${DATA_DIR}/${DATASETS:folder}/${PART_TYPES:annotations}/features

PART_TYPES:
  GLOBAL:         &parts_global
    feature_suffix: ""

  UNI:            &parts_uni
    is_uniform: true
    feature_suffix: _26parts_uniform
    rescale_size: !!int -1
    scales:
     - 0.2

  GT:             &parts_gt
    feature_suffix: _16parts_gt
    rescale_size: !!int -1
    scales:
     - 0.31

  GT2:            &parts_gt2
    # TODO: should be changed, since GT2 parts can also be present for other datasets
    annotations: cub200_11_regrouped
    feature_suffix: _5parts_gt
    rescale_size: !!int -1
    scales:
      - 0.31

  NAC:            &parts_nac
    annotations: NAC/2017-bilinear
    feature_suffix: _20parts
    rescale_size: !!int 224
    scales:
      - 0.31
      - 0.45

  L1_pred:        &parts_l1p
    annotations: L1_pred
    feature_suffix: _5parts_L1_pred
    rescale_size: !!int 427
    scales: []

  L1_full:        &parts_l1f
    annotations: L1_full
    feature_suffix: _5parts_L1_full
    rescale_size: !!int 427
    scales: []

  NTS:        &parts_nts
    annotations: NTS
    feature_suffix: _7parts_nts
    rescale_size: !!int 448
    scales: []

  NTS2:        &parts_nts2
    annotations: NTS2
    feature_suffix: _5parts_nts
    rescale_size: !!int 448
    scales: []

PARTS:
  # all <DATASET>_<PART_TYPES> combinations are created implicitely.
  # if you want some changes in the configs, then crete here an entry
  # and update the config values

  ####################################
  # CUB200-2011
  ####################################

  CUB200_L1_pred:
    <<: *cub200
    <<: *parts_l1p
    annotations: cub200_11_L1_pred_old
    rescale_size: !!int 299

  CUB200_L1_full:
    <<: *cub200
    <<: *parts_l1f
    annotations: cub200_11_L1_full_old
    rescale_size: !!int 299

  CUB200_L1_pred_15:
    <<: *cub200
    <<: *parts_l1p
    annotations: L1_pred_15
    feature_suffix: _15parts_L1_pred

  CUB200_L1_full_15:
    <<: *cub200
    <<: *parts_l1f
    annotations: L1_full_15
    feature_suffix: _15parts_L1_full

  CUB200_L1_pred_2:
    <<: *cub200
    <<: *parts_l1p
    annotations: L1_pred_2
    feature_suffix: _2parts_L1_pred

  CUB200_L1_full_2:
    <<: *cub200
    <<: *parts_l1f
    annotations: L1_full_2
    feature_suffix: _2parts_L1_full

  ####################################
  # iNaturalist 2019
  ####################################

  INAT19_MINI_L1_pred:
    <<: *inat19_mini
    <<: *parts_l1p
    annotations: 2019_small_L1_pred
    feature_suffix: _5parts_L1_pred.mini

  INAT19_MINI_L1_full:
    <<: *inat19_mini
    <<: *parts_l1f
    annotations: 2019_small_L1_full
    feature_suffix: _5parts_L1_full.mini

  INAT19_TEST_L1_pred:
    <<: *inat19_test
    <<: *parts_l1p
    annotations: 2019_test_L1_pred
    feature_suffix: _5parts_L1_pred.test

  INAT19_TEST_L1_full:
    <<: *inat19_test
    <<: *parts_l1f
    annotations: 2019_test_L1_full
    feature_suffix: _5parts_L1_full.test