公开数据集
数据结构 ? 0.05M
Data Structure ?
* 以上分析是由系统提取分析形成的结果,具体实际数据为准。
README.md
This dataset contains run time statistics and details about scores for the first development round of [NIPS 2017 Adversarial learning competition](https://www.kaggle.com/nips-2017-adversarial-learning-competition)
## Content
Matrices with intermediate results
Following matrices with intermediate results are provided:
* **accuracy_matrix.csv** - matrix with number of correctly classified images for each pair of attack (targeted and non-targeted) and defense
* **error_matrix.csv** - matrix with number of misclassified images for each pair of attack (targeted and non-targeted) and defense
* **hit_target_class_matrix.csv** - matrix with number of times image was classified as specific target class for each pair of attack (targeted and non-targeted) and defense
In each of these matrices, rows correspond to defenses, columns correspond to attack. Also first row and column are headers with Kaggle Team IDs (or baseline ID).
Scores and run time statistics of submissions
Following files contain scores and run time stats of the submissions:
* **non_targeted_attack_results.csv** - scores and run time statistics of all non-targeted attacks
* **targeted_attack_results.csv** - scores and run time statistics of all targeted attacks
* **defense_results.csv** - scores and run time statistics of all defenses
Each row of these files correspond to one submission. Columns have following meaning:
* KaggleTeamId - either Kaggle Team ID or ID of the baseline.
* TeamName - human readable team name
* Score - raw score of the submission
* NormalizedScore - normalized (to be between 0 and 1) score of the submission
* MinEvalTime - minimum evaluation time of 100 images
* MaxEvalTime - maximum evaluation time of 100 images
* MedianEvalTime - median evaluation time of 100 images
* MeanEvalTime - average evaluation time of 100 images
## Notes about the data
* Due to team merging these files contain slightly more submissions than reflected in leaderboard.
* Also not all attacks were used to compute scores of defenses and not all defenses were used to compute scores of attacks. Thus if you simply sum-up values in rows/columns of the corresponding matrix you won't obtain exact score of the submission (however number you obtain will be very close to actual score).
×
帕依提提提温馨提示
该数据集正在整理中,为您准备了其他渠道,请您使用
注:部分数据正在处理中,未能直接提供下载,还请大家理解和支持。
暂无相关内容。
暂无相关内容。
- 分享你的想法
去分享你的想法~~
全部内容
欢迎交流分享
开始分享您的观点和意见,和大家一起交流分享.
数据使用声明:
- 1、该数据来自于互联网数据采集或服务商的提供,本平台为用户提供数据集的展示与浏览。
- 2、本平台仅作为数据集的基本信息展示、包括但不限于图像、文本、视频、音频等文件类型。
- 3、数据集基本信息来自数据原地址或数据提供方提供的信息,如数据集描述中有描述差异,请以数据原地址或服务商原地址为准。
- 1、本站中的所有数据集的版权都归属于原数据发布者或数据提供方所有。
- 1、如您需要转载本站数据,请保留原数据地址及相关版权声明。
- 1、如本站中的部分数据涉及侵权展示,请及时联系本站,我们会安排进行数据下线。