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Holmeister/tiny_imagenet_TRADES_pgd_fawa_ccm_True_ccr_True_results

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Hugging Face2024-05-20 更新2024-06-12 收录
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https://hf-mirror.com/datasets/Holmeister/tiny_imagenet_TRADES_pgd_fawa_ccm_True_ccr_True_results
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资源简介:
--- dataset_info: features: - name: '0' dtype: float64 - name: '1' dtype: float64 - name: '2' dtype: float64 - name: '3' dtype: float64 - name: '4' dtype: float64 - name: '5' dtype: float64 - name: '6' dtype: float64 - name: '7' dtype: float64 - name: '8' dtype: float64 - name: '9' dtype: float64 - name: '10' dtype: float64 - name: '11' dtype: float64 - name: '12' dtype: float64 - name: '13' dtype: float64 - name: '14' dtype: float64 - name: '15' dtype: float64 - name: '16' dtype: float64 - name: '17' dtype: float64 - name: '18' dtype: float64 - name: '19' dtype: float64 - name: '20' dtype: float64 - name: '21' dtype: float64 - name: '22' dtype: float64 - name: '23' dtype: float64 - name: '24' dtype: float64 - name: '25' dtype: float64 - name: '26' dtype: float64 - name: '27' dtype: float64 - name: '28' dtype: float64 - name: '29' dtype: float64 - name: '30' dtype: float64 - name: '31' dtype: float64 - name: '32' dtype: float64 - name: '33' dtype: float64 - name: '34' dtype: float64 - name: '35' dtype: float64 - name: '36' dtype: float64 - name: '37' dtype: float64 - name: '38' dtype: float64 - name: '39' dtype: float64 - name: '40' dtype: float64 - name: '41' dtype: float64 - name: '42' dtype: float64 - name: '43' dtype: float64 - name: '44' dtype: float64 - name: '45' dtype: float64 - name: '46' dtype: float64 - name: '47' dtype: float64 - name: '48' dtype: float64 - name: '49' dtype: float64 - name: '50' dtype: float64 - name: '51' dtype: float64 - name: '52' dtype: float64 - name: '53' dtype: float64 - name: '54' dtype: float64 - name: '55' dtype: float64 - name: '56' dtype: float64 - name: '57' dtype: float64 - name: '58' dtype: float64 - name: '59' dtype: float64 - name: '60' dtype: float64 - name: '61' dtype: float64 - name: '62' dtype: float64 - name: '63' dtype: float64 - name: '64' dtype: float64 - name: '65' dtype: float64 - name: '66' dtype: float64 - name: '67' dtype: float64 - name: '68' dtype: float64 - name: '69' dtype: float64 - name: '70' dtype: float64 - name: '71' dtype: float64 - name: '72' dtype: float64 - name: '73' dtype: float64 - name: '74' dtype: float64 - name: '75' dtype: float64 - name: '76' dtype: float64 - name: '77' dtype: float64 - name: '78' dtype: float64 - name: '79' dtype: float64 - name: '80' dtype: float64 - name: '81' dtype: float64 - name: '82' dtype: float64 - name: '83' dtype: float64 - name: '84' dtype: float64 - name: '85' dtype: float64 - name: '86' dtype: float64 - name: '87' dtype: float64 - name: '88' dtype: float64 - name: '89' dtype: float64 - name: '90' dtype: float64 - name: '91' dtype: float64 - name: '92' dtype: float64 - name: '93' dtype: float64 - name: '94' dtype: float64 - name: '95' dtype: float64 - name: '96' dtype: float64 - name: '97' dtype: float64 - name: '98' dtype: float64 - name: '99' dtype: float64 - name: '100' dtype: float64 - name: '101' dtype: float64 - name: '102' dtype: float64 - name: '103' dtype: float64 - name: '104' dtype: float64 - name: '105' dtype: float64 - name: '106' dtype: float64 - name: '107' dtype: float64 - name: '108' dtype: float64 - name: '109' dtype: float64 - name: '110' dtype: float64 - name: '111' dtype: float64 - name: '112' dtype: float64 - name: '113' dtype: float64 - name: '114' dtype: float64 - name: '115' dtype: float64 - name: '116' dtype: float64 - name: '117' dtype: float64 - name: '118' dtype: float64 - name: '119' dtype: float64 - name: '120' dtype: float64 - name: '121' dtype: float64 - name: '122' dtype: float64 - name: '123' dtype: float64 - name: '124' dtype: float64 - name: '125' dtype: float64 - name: '126' dtype: float64 - name: '127' dtype: float64 - name: '128' dtype: float64 - name: '129' dtype: float64 - name: '130' dtype: float64 - name: '131' dtype: float64 - name: '132' dtype: float64 - name: '133' dtype: float64 - name: '134' dtype: float64 - name: '135' dtype: float64 - name: '136' dtype: float64 - name: '137' dtype: float64 - name: '138' dtype: float64 - name: '139' dtype: float64 - name: '140' dtype: float64 - name: '141' dtype: float64 - name: '142' dtype: float64 - name: '143' dtype: float64 - name: '144' dtype: float64 - name: '145' dtype: float64 - name: '146' dtype: float64 - name: '147' dtype: float64 - name: '148' dtype: float64 - name: '149' dtype: float64 - name: '150' dtype: float64 - name: '151' dtype: float64 - name: '152' dtype: float64 - name: '153' dtype: float64 - name: '154' dtype: float64 - name: '155' dtype: float64 - name: '156' dtype: float64 - name: '157' dtype: float64 - name: '158' dtype: float64 - name: '159' dtype: float64 - name: '160' dtype: float64 - name: '161' dtype: float64 - name: '162' dtype: float64 - name: '163' dtype: float64 - name: '164' dtype: float64 - name: '165' dtype: float64 - name: '166' dtype: float64 - name: '167' dtype: float64 - name: '168' dtype: float64 - name: '169' dtype: float64 - name: '170' dtype: float64 - name: '171' dtype: float64 - name: '172' dtype: float64 - name: '173' dtype: float64 - name: '174' dtype: float64 - name: '175' dtype: float64 - name: '176' dtype: float64 - name: '177' dtype: float64 - name: '178' dtype: float64 - name: '179' dtype: float64 - name: '180' dtype: float64 - name: '181' dtype: float64 - name: '182' dtype: float64 - name: '183' dtype: float64 - name: '184' dtype: float64 - name: '185' dtype: float64 - name: '186' dtype: float64 - name: '187' dtype: float64 - name: '188' dtype: float64 - name: '189' dtype: float64 - name: '190' dtype: float64 - name: '191' dtype: float64 - name: '192' dtype: float64 - name: '193' dtype: float64 - name: '194' dtype: float64 - name: '195' dtype: float64 - name: '196' dtype: float64 - name: '197' dtype: float64 - name: '198' dtype: float64 - name: '199' dtype: float64 splits: - name: train num_bytes: 6400 num_examples: 4 download_size: 95794 dataset_size: 6400 configs: - config_name: default data_files: - split: train path: data/train-* ---

This dataset includes 200 numerical features (from 0 to 199), each of type float64. The dataset has one split named train with 4 examples and a size of 6400 bytes. The download size and dataset size are 95794 bytes and 6400 bytes respectively.
提供机构:
Holmeister
原始信息汇总

数据集概述

数据集特征

  • 特征数量: 200个
  • 特征类型: 所有特征均为float64类型

数据集划分

  • 训练集:
    • 样本数量: 4个
    • 数据大小: 6400字节

数据集大小

  • 下载大小: 95794字节
  • 数据集大小: 6400字节
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