Datasets:
File size: 42,115 Bytes
7debf8d d35d646 383573d d35d646 7debf8d 1baba94 d35d646 7debf8d 1baba94 7debf8d 1baba94 7debf8d 1baba94 383573d 7debf8d 1baba94 7debf8d 1baba94 7debf8d 383573d 7debf8d 383573d 7debf8d d35d646 7debf8d 1baba94 7debf8d 1baba94 7debf8d d35d646 383573d 7debf8d d35d646 8bbf637 1baba94 8bbf637 d35d646 8bbf637 d35d646 8bbf637 d35d646 8bbf637 d35d646 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 7debf8d 1baba94 7debf8d 383573d 1baba94 7debf8d 383573d 7debf8d 1baba94 7debf8d 383573d 7debf8d 383573d 7debf8d 1baba94 383573d 1baba94 7debf8d 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d 1baba94 383573d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 |
# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""The ELEVATER benchmark"""
import json
import os
import datasets
from zipfile import ZipFile
from io import BytesIO
from PIL import Image
_VERSION = "1.0.0"
_BASE_URL = "https://cvinthewildeus.blob.core.windows.net/datasets/"
_FEW_SHOTS_FILE_PATH="subidx/id_label/data_train_#shot/"
_ELEVATER_CITATION = """\
@article{li2022elevater,
title={ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models},
author={Li, Chunyuan and Liu, Haotian and Li, Liunian Harold and Zhang, Pengchuan and Aneja, Jyoti and Yang, Jianwei and Jin, Ping and Lee, Yong Jae and Hu, Houdong and Liu, Zicheng and Gao, Jianfeng},
journal={Neural Information Processing Systems},
year={2022}
}
Note that each ELEVATER dataset has its own citation. Please see the source to
get the correct citation for each contained dataset.
"""
_CIFAR_10_CITATION="""\
@article{krizhevsky2009learning,
title={Learning multiple layers of features from tiny images},
author={Krizhevsky, Alex and Hinton, Geoffrey and others},
year={2009},
publisher={Toronto, ON, Canada}
}"""
_VOC_2007_CLASSIFICATION_CITATION="""\
@misc{pascal-voc-2007,
author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.",
title = "The {PASCAL} {V}isual {O}bject {C}lasses {C}hallenge 2007 {(VOC2007)} {R}esults",
howpublished = "http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html"
}"""
_GTSRB_CITATION="""\
@inproceedings{Houben-IJCNN-2013,
author = {Sebastian Houben and Johannes Stallkamp and Jan Salmen and Marc Schlipsing and Christian Igel},
booktitle = {International Joint Conference on Neural Networks},
title = {Detection of Traffic Signs in Real-World Images: The {G}erman {T}raffic {S}ign {D}etection {B}enchmark},
number = {1288},
year = {2013},
}"""
_COUNTRY211_CITATION="""\
@inproceedings{radford2021learning,
title={Learning transferable visual models from natural language supervision},
author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
booktitle={International Conference on Machine Learning},
pages={8748--8763},
year={2021},
organization={PMLR}
}"""
_RENDERED_SST2_CITATION="""\
@inproceedings{radford2021learning,
title={Learning transferable visual models from natural language supervision},
author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
booktitle={International Conference on Machine Learning},
pages={8748--8763},
year={2021},
organization={PMLR}
}"""
_KITTI_DISTANCE_CITATION="""\
@inproceedings{fritsch2013new,
title={A new performance measure and evaluation benchmark for road detection algorithms},
author={Fritsch, Jannik and Kuehnl, Tobias and Geiger, Andreas},
booktitle={16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)},
pages={1693--1700},
year={2013},
organization={IEEE}
}"""
_EOROSAT_CLIP_CITATION="""\
@article{helber2019eurosat,
title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
volume={12},
number={7},
pages={2217--2226},
year={2019},
publisher={IEEE}
}"""
_RESISC45_CLIP_CITATION="""\
@article{cheng2017remote,
title={Remote sensing image scene classification: Benchmark and state of the art},
author={Cheng, Gong and Han, Junwei and Lu, Xiaoqiang},
journal={Proceedings of the IEEE},
volume={105},
number={10},
pages={1865--1883},
year={2017},
publisher={IEEE}
}"""
_CALTECH_101_CITATION="""\
@inproceedings{fei2004learning,
title={Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories},
author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
booktitle={2004 conference on computer vision and pattern recognition workshop},
pages={178--178},
year={2004},
organization={IEEE}
}"""
_CIFAR_100_CITATION="""\
@article{krizhevsky2009learning,
title={Learning multiple layers of features from tiny images},
author={Krizhevsky, Alex and Hinton, Geoffrey and others},
year={2009},
publisher={Toronto, ON, Canada}
}"""
_DTD_CITATION="""\
@inproceedings{cimpoi2014describing,
title={Describing textures in the wild},
author={Cimpoi, Mircea and Maji, Subhransu and Kokkinos, Iasonas and Mohamed, Sammy and Vedaldi, Andrea},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={3606--3613},
year={2014}
}"""
_FGVC_AIRCRAFT_2013B_VARIANTS102_CITATION="""\
@article{maji2013fine,
title={Fine-grained visual classification of aircraft},
author={Maji, Subhransu and Rahtu, Esa and Kannala, Juho and Blaschko, Matthew and Vedaldi, Andrea},
journal={arXiv preprint arXiv:1306.5151},
year={2013}
}"""
_FOOD_101_CITATION="""\
@inproceedings{bossard2014food,
title={Food-101--mining discriminative components with random forests},
author={Bossard, Lukas and Guillaumin, Matthieu and Gool, Luc Van},
booktitle={European conference on computer vision},
pages={446--461},
year={2014},
organization={Springer}
}"""
_MNIST_CITATION="""\
@article{deng2012mnist,
title={The mnist database of handwritten digit images for machine learning research [best of the web]},
author={Deng, Li},
journal={IEEE signal processing magazine},
volume={29},
number={6},
pages={141--142},
year={2012},
publisher={IEEE}
}"""
_OXFORD_FLOWER_102_CITATION="""\
@inproceedings{nilsback2008automated,
title={Automated flower classification over a large number of classes},
author={Nilsback, Maria-Elena and Zisserman, Andrew},
booktitle={2008 Sixth Indian Conference on Computer Vision, Graphics \& Image Processing},
pages={722--729},
year={2008},
organization={IEEE}
}"""
_OXFORD_IIIT_PETS_CITATION="""\
@inproceedings{parkhi2012cats,
title={Cats and dogs},
author={Parkhi, Omkar M and Vedaldi, Andrea and Zisserman, Andrew and Jawahar, CV},
booktitle={2012 IEEE conference on computer vision and pattern recognition},
pages={3498--3505},
year={2012},
organization={IEEE}
}"""
_PATCH_CAMELYON_CITATION="""\
@inproceedings{veeling2018rotation,
title={Rotation equivariant CNNs for digital pathology},
author={Veeling, Bastiaan S and Linmans, Jasper and Winkens, Jim and Cohen, Taco and Welling, Max},
booktitle={International Conference on Medical image computing and computer-assisted intervention},
pages={210--218},
year={2018},
organization={Springer}
}"""
_STANFORD_CARS_CITATION="""\
@inproceedings{krause20133d,
title={3d object representations for fine-grained categorization},
author={Krause, Jonathan and Stark, Michael and Deng, Jia and Fei-Fei, Li},
booktitle={Proceedings of the IEEE international conference on computer vision workshops},
pages={554--561},
year={2013}
}"""
_FER_2013_CITATION="""\
@misc{challenges-in-representation-learning-facial-expression-recognition-challenge,
author = {Dumitru, Ian Goodfellow, Yoshua Bengio},
title = {Challenges in Representation Learning: Facial Expression Recognition Challenge},
publisher = {Kaggle},
year = {2013},
url = {https://kaggle.com/competitions/challenges-in-representation-learning-facial-expression-recognition-challenge}
}"""
_HATEFUL_MEMES_CITATION="""\
@article{kiela2020hateful,
title={The hateful memes challenge: Detecting hate speech in multimodal memes},
author={Kiela, Douwe and Firooz, Hamed and Mohan, Aravind and Goswami, Vedanuj and Singh, Amanpreet and Ringshia, Pratik and Testuggine, Davide},
journal={Advances in Neural Information Processing Systems},
volume={33},
pages={2611--2624},
year={2020}
}"""
class ELEVATERConfig(datasets.BuilderConfig):
"""BuilderConfig for ELEVATER."""
def __init__(self, name, description, contact, version, type_, format_,
root_folder, labelmap, num_classes, train, val, test, few_shots_file_path,
citation, url, num_shots, random_seed, **kwargs):
"""BuilderConfig for ELEVATER.
Args:
features: `list[string]`, list of the features that will appear in the
feature dict. Should not include "label".
data_url: `string`, url to download the zip file from.
citation: `string`, citation for the data set.
url: `string`, url for information about the data set.
label_classes: `list[string]`, the list of classes for the label if the
label is present as a string. Non-string labels will be cast to either
'False' or 'True'.
**kwargs: keyword arguments forwarded to super.
"""
super(ELEVATERConfig, self).__init__(**kwargs)
self.name = name
self.description = description
self.contact = contact
self.version = version
self.type = type_
self.format = format_
self.root_folder = root_folder
self.labelmap = labelmap
self.num_classes = num_classes
self.train = train
self.val = val
self.test = test
self.few_shots_file_path = few_shots_file_path
self.citation = citation
self.url = url
self.num_shots = num_shots
self.random_seed = random_seed
class ELEVATER(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
ELEVATERConfig(
name="cifar-10",
description="The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/cifar_10_20211007",
labelmap="labels.txt",
num_classes=10,
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 50000
},
val=None,
test={
"index_path": "test.txt",
"files_for_local_usage": ["val.zip"],
"num_images": 10000
},
citation=_CIFAR_10_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="voc-2007-classification",
description="Voc2007 classification dataset.",
contact="pinjin",
version=_VERSION,
type_="classification_multilabel",
format_=None,
root_folder="classification/voc2007_20211007",
train={
"index_path": "train_ic.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 2501
},
val={
"index_path": "val_ic.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 2510
},
test={
"index_path": "test_ic.txt",
"files_for_local_usage": ["test.zip"],
"num_images": 4952
},
labelmap="labels.txt",
num_classes=20,
citation=_VOC_2007_CLASSIFICATION_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="gtsrb",
description="The German Traffic Sign Recognition Benchmark (GTSRB) is a multi-class image classification benchmark in the domain of advanced driver assistance systems and autonomous driving. It was first published at IJCNN 2011.",
contact=None,
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/gtsrb_20210923",
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 26640
},
val={
"index_path": "val.txt",
"files_for_local_usage": ["val.zip"],
"num_images": 12569
},
test={
"index_path": "test.txt",
"files_for_local_usage": ["final_test.zip"],
"num_images": 12630
},
labelmap="labelmap.txt",
num_classes=43,
citation=_GTSRB_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="country211",
description="Country211 is an internal OpenAI dataset designed to assess the geolocation capability of visual representations. It filters the YFCC100m dataset (Thomee et al., 2016) to find 211 countries (defined as having an ISO-3166 country code) that have at least 300 photos with GPS coordinates. OpenAI built a balanced dataset with 211 categories, by sampling 200 photos for training and 100 photos for testing, for each country.",
contact=None,
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/country211_20210924",
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 31650
},
val={
"index_path": "valid.txt",
"files_for_local_usage": ["valid.zip"],
"num_images": 10550
},
test={
"index_path": "test.txt",
"files_for_local_usage": ["test.zip"],
"num_images": 21100
},
labelmap="labels.txt",
num_classes=211,
citation=_COUNTRY211_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="rendered-sst2",
description="Dataset is from CLIP: The Rendered SST2 dataset is designed to measure the optical character recognition capability of visual representations. To do so, we used the sentences from the Stanford Sentiment Treebank dataset (Socher et al., 2013) and rendered them into images, with black texts on a white background, in a 448×448 resolution.",
contact=None,
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/rendered_sst2_20210924",
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 6920
},
val={
"index_path": "valid.txt",
"files_for_local_usage": ["valid.zip"],
"num_images": 827
},
test={
"index_path": "test.txt",
"files_for_local_usage": ["test.zip"],
"num_images": 1821
},
labelmap="labels.txt",
num_classes=2,
citation=_RENDERED_SST2_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="kitti-distance",
description="The kitti-distance dataset was taken from the VTAB benchmark, and the task was to predict how distant a vehicle is in the photo. More details: https://github.com/openai/CLIP/issues/86",
contact=None,
version=_VERSION,
type_="classification_multiclass",
format_="coco",
root_folder="classification/kitti_distance_20210923",
train={
"index_path": "train_meta.json",
"files_for_local_usage": ["train_images.zip"],
"num_images": 6347
},
val={
"index_path": "validation_meta.json",
"files_for_local_usage": ["validation_images.zip"],
"num_images": 423
},
test={
"index_path": "test_meta.json",
"files_for_local_usage": ["test_images.zip"],
"num_images": 711
},
labelmap=None,
num_classes=4,
citation=_KITTI_DISTANCE_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="eurosat_clip",
description="Dataset sampled by CLIP from Eurosat (EuroSAT dataset is based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.), see: https://github.com/openai/CLIP/issues/45",
contact=None,
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/eurosat_clip_20210930",
train={
"index_path": "train.txt",
"files_for_local_usage": ["2750.zip"],
"num_images": 5000
},
val={
"index_path": "val.txt",
"files_for_local_usage": ["2750.zip"],
"num_images": 5000
},
test={
"index_path": "test.txt",
"files_for_local_usage": ["2750.zip"],
"num_images": 5000
},
labelmap="labels.txt",
num_classes=10,
citation=_EOROSAT_CLIP_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="resisc45_clip",
description="Dataset sampled by CLIP, see: https://github.com/openai/CLIP/issues/45. RESISC45 dataset is a publicly available benchmark for Remote Sensing Image Scene Classification",
contact=None,
version=_VERSION,
type_="classification_multiclass",
format_="coco",
root_folder="classification/resisc45_clip_20210924",
train={
"index_path": "train.json",
"files_for_local_usage": ["images.zip"],
"num_images": 3150
},
val={
"index_path": "val.json",
"files_for_local_usage": ["images.zip"],
"num_images": 3150
},
test={
"index_path": "test.json",
"files_for_local_usage": ["images.zip"],
"num_images": 25200
},
labelmap="labels.txt",
num_classes=45,
citation=_RESISC45_CLIP_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="caltech-101",
description="Pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images. Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc 'Aurelio Ranzato. The size of each image is roughly 300 x 200 pixels. ",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/caltech_101_20211007",
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 3060
},
val=None,
test={
"index_path": "test.txt",
"files_for_local_usage": ["test.zip"],
"num_images": 6084
},
labelmap=None,
num_classes=45,
citation=_CALTECH_101_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="cifar-100",
description="This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a 'fine' label (the class to which it belongs) and a 'coarse' label (the superclass to which it belongs).",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/cifar100_20200721",
train={
"index_path": "train_images.txt",
"files_for_local_usage": ["train_images.zip"],
"num_images": 50000
},
val=None,
test={
"index_path": "test_images.txt",
"files_for_local_usage": ["test_images.zip"],
"num_images": 10000
},
labelmap="labels.txt",
num_classes=100,
citation=_CIFAR_100_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="dtd",
description="The Describable Textures Dataset (DTD) is an evolving collection of textural images in the wild, annotated with a series of human-centric attributes, inspired by the perceptual properties of textures. This data is made available to the computer vision community for research purposes.",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/dtd_20211007",
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 1880
},
val={
"index_path": "val.txt",
"files_for_local_usage": ["val.zip"],
"num_images": 1880
},
test={
"index_path": "test.txt",
"files_for_local_usage": ["test.zip"],
"num_images": 1880
},
labelmap="labels.txt",
num_classes=47,
citation=_DTD_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="fgvc-aircraft-2013b-variants102",
description="Fine-Grained Visual Classification of Aircraft (FGVC-Aircraft) is a benchmark dataset for the fine grained visual categorization of aircraft.",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/fgvc_aircraft_2013b_variants102_20211007",
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 3334
},
val={
"index_path": "val.txt",
"files_for_local_usage": ["val.zip"],
"num_images": 3333
},
test={
"index_path": "test.txt",
"files_for_local_usage": ["test.zip"],
"num_images": 3333
},
labelmap="labels.txt",
num_classes=100,
citation=_FGVC_AIRCRAFT_2013B_VARIANTS102_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="food-101",
description="This dataset consists of 101 food categories, with 101000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/food_101_20211007",
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 75750
},
val=None,
test={
"index_path": "test.txt",
"files_for_local_usage": ["val.zip"],
"num_images": 25250
},
labelmap="labels.txt",
num_classes=101,
citation=_FOOD_101_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="mnist",
description="The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/mnist_20211008",
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 60000
},
val=None,
test={
"index_path": "test.txt",
"files_for_local_usage": ["val.zip"],
"num_images": 10000
},
labelmap="labels.txt",
num_classes=10,
citation=_MNIST_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="oxford-flower-102",
description="A dataset consisting of 102 flower categories. The flowers chosen to be flower commonly occuring in the United Kingdom. Each class consists of between 40 and 258 images.",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/oxford_flower_102_20211007",
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 1020
},
val={
"index_path": "val.txt",
"files_for_local_usage": ["val.zip"],
"num_images": 1020
},
test={
"index_path": "test.txt",
"files_for_local_usage": ["test.zip"],
"num_images": 6149
},
labelmap="labels.txt",
num_classes=102,
citation=_OXFORD_FLOWER_102_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="oxford-iiit-pets",
description="A 37-category pet dataset with roughly 200 images for each class. The images have a large variations in scale, pose and lighting.",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/oxford_iiit_pets_20211007",
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 3680
},
val=None,
test={
"index_path": "test.txt",
"files_for_local_usage": ["val.zip"],
"num_images": 3669
},
labelmap="labels.txt",
num_classes=37,
citation=_OXFORD_IIIT_PETS_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="patch-camelyon",
description="The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than imagenet, trainable on a single GPU.",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/patch_camelyon_20211007",
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 262144
},
val={
"index_path": "val.txt",
"files_for_local_usage": ["validation.zip"],
"num_images": 32768
},
test={
"index_path": "test.txt",
"files_for_local_usage": ["test.zip"],
"num_images": 32768
},
labelmap="labels.txt",
num_classes=2,
citation=_PATCH_CAMELYON_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="stanford-cars",
description="The Cars dataset contains 16,185 images of 196 classes of cars. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe.",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/stanford_cars_20211007",
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 8144
},
val=None,
test={
"index_path": "test.txt",
"files_for_local_usage": ["val.zip"],
"num_images": 8041
},
labelmap="labels.txt",
num_classes=196,
citation=_STANFORD_CARS_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="fer-2013",
description="The data consists of 48x48 pixel grayscale images of faces. The task is to categorize each face based on the emotion shown in the facial expression into one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral).",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
format_=None,
root_folder="classification/fer_2013_20211008",
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 28709
},
val={
"index_path": "val.txt",
"files_for_local_usage": ["val.zip"],
"num_images": 3589
},
test={
"index_path": "test.txt",
"files_for_local_usage": ["test.zip"],
"num_images": 3589
},
labelmap="labels.txt",
num_classes=7,
citation=_FER_2013_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
ELEVATERConfig(
name="hateful-memes",
description="At the massive scale of the internet, the task of detecting multimodal hate is both extremely important and particularly difficult. Relying on just text or just images to determine whether a meme is hateful is insufficient. By using certain types of images, text, or combinations, a meme can become a multimodal type of hate speech.",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
format_="coco",
root_folder="classification/hateful_memes_20211014",
train={
"index_path": "train_meta.json",
"files_for_local_usage": ["img.zip"],
"num_images": 8500
},
val=None,
test={
"index_path": "test_meta.json",
"files_for_local_usage": ["img.zip"],
"num_images": 500
},
labelmap="labels.txt",
num_classes=2,
citation=_HATEFUL_MEMES_CITATION,
url=_BASE_URL,
few_shots_file_path=_FEW_SHOTS_FILE_PATH,
num_shots=-1, # 5, 20, 50
random_seed=-1, # 0, 1, 2
),
]
def _info(self):
if self.config.name == "voc-2007-classification":
features = datasets.Features(
{
"image_file_path": datasets.Value("string"),
"image": datasets.Image(),
"labels": [datasets.Value("int32")]
}
)
else:
features = datasets.Features(
{
"image_file_path": datasets.Value("string"),
"image": datasets.Image(),
"labels": datasets.Value("int32")
}
)
return datasets.DatasetInfo(
description=self.config.description,
features=features,
citation=self.config.citation + '\n' + _ELEVATER_CITATION,
)
def _split_generators(self, dl_manager):
_URL = self.config.url + self.config.root_folder
urls_to_download = {
"train": {
"images": os.path.join(_URL, self.config.train['files_for_local_usage'][0]),
"index": os.path.join(_URL, self.config.train['index_path']),
},
"test": {
"images": os.path.join(_URL, self.config.test['files_for_local_usage'][0]),
"index": os.path.join(_URL, self.config.test['index_path']),
}
}
if self.config.num_shots in [5, 20, 50]:
assert self.config.random_seed in [0, 1, 2]
few_shots_file_path_temp = _FEW_SHOTS_FILE_PATH.replace('#', str(self.config.num_shots))
file_name = 'shot' + str(self.config.num_shots) + '_seed' + str(self.config.random_seed) + '.json'
few_shot_path = os.path.join(_BASE_URL, few_shots_file_path_temp, self.config.name, file_name)
urls_to_download["train"]["few_shot"] = few_shot_path
else:
pass
# if self.config.val is not None:
# urls_to_download['val'] = {
# "images": os.path.join(_URL, self.config.val['files_for_local_usage'][0]),
# "index": os.path.join(_URL, self.config.val['index_path']),
# }
downloaded_files = dl_manager.download_and_extract(urls_to_download)
try:
few_shot_train_file = downloaded_files["train"]["few_shot"]
except:
few_shot_train_file = None
SplitGenerator_list = [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": downloaded_files["train"]["images"],
"index": downloaded_files["train"]["index"],
"few_shot": few_shot_train_file,
"split": datasets.Split.TRAIN,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"images": downloaded_files["test"]["images"],
"index": downloaded_files["test"]["index"],
"few_shot": None,
"split": datasets.Split.TEST,
},
)]
# if self.config.val is not None:
# SplitGenerator_list.append(datasets.SplitGenerator(
# name=datasets.Split.VALIDATION,
# gen_kwargs={
# "images": downloaded_files["val"]["images"],
# "index": downloaded_files["val"]["index"],
# "split": datasets.Split.VALIDATION,
# },
# ))
return SplitGenerator_list
def _generate_examples(self, images, index, few_shot, split):
if few_shot is not None:
few_shot_images = []
with open(few_shot, encoding="utf-8") as f:
data = json.load(f)
for item in data:
few_shot_images.append(item['id'].split('@')[-1])
if self.config.name in ["kitti-distance", "resisc45_clip", "hateful-memes"]:
with open(index, encoding="utf-8") as f:
data = json.load(f)
for i in range(len(data['images'])):
label = data['annotations'][i]['category_id']
path_temp = data['images'][i]['file_name'].split('@')[1]
path = os.path.join(images, path_temp)
if few_shot is not None:
if path_temp in few_shot_images:
yield i, {
"image_file_path": path,
"image": path,
"labels": label,
}
else:
yield i, {
"image_file_path": path,
"image": path,
"labels": label,
}
else:
with open(index, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
line_split = line[:-1].split(" ")
if len(line_split) > 3:
image_path_temp = " ".join(line_split[:-1])
path_temp = image_path_temp.split('@')[1]
else:
path_temp = line_split[0].split('@')[1]
path = os.path.join(images, path_temp)
if self.config.type == "classification_multilabel":
label = [int(x) for x in line_split[-1].split(',')]
else:
try:
label = int(line_split[1])
except:
if self.config.name == "eurosat_clip" and split == 'test':
label = 9
if few_shot is not None:
if path_temp in few_shot_images:
yield i, {
"image_file_path": path,
"image": path,
"labels": label,
}
else:
pass
else:
yield i, {
"image_file_path": path,
"image": path,
"labels": label,
}
|