对LFW数据库的翻译

原文：
Training, Validation, and Testing:
View 1: For development purposes, we recommend using the below training/testing split, which was generated randomly and independently of the splits for 10-fold cross validation, to avoid unfairly overfitting to the sets above during development. For instance, these sets may be viewed as a model selection set and a validation set. See the tech report below for more details.
Explore the sets: [training][test]
Download the sets: pairsDevTrain.txt, pairsDevTest.txt, peopleDevTrain.txt, peopleDevTest.txt

译文：
训练， 验证和测试
视角1： 处于发展的目的，我们推荐使用如下的训练/测试 分割， 随机生成且独立的10组交叉验证的分法，可以防止在优化过程中，出现过度拟合的现象。举例，这些集合能被当做模型选择集和验证集。 看如下技术报告的具体说明。
看集合： [训练集] [测试集]
下载集合： 训练集合对.txt, 测试集合对.txt, 训练集合人名.txt, 测试集合人名.txt

View 2: As a benchmark for comparison, we suggest reporting performance as 10-fold cross validation using splits we have randomly generated.
Explore the sets: 1 [2] [3] [4] [5] [6] [7] [8][9][10]
Download the sets: pairs.txt, people.txt
For information on the file formats, please refer to the README above.
For details on how the sets were created, please refer to the tech report below.

视角2： 作为比对基准，我们建议用随机生成的10组交叉验证集合，做性能报告输出。
看集合：1 [2] [3] [4] [5] [6] [7] [8][9][10]
下载集合： 集合对.txt , 人名.txt
对于文件格式的信息，请见上面的 README文件；
对于这些集合是如何产生的，请参见下面的技术报告；

Results:
Accuracy and ROC curves for various methods available on results page.
Information:
13233 images
5749 people
1680 people with two or more images

对于几种不同方法的精度和ROC曲线见results页面。
信息：
13233 图片
5749 人
1680 人，拥2张以上的图

Errata:
The following is a list of known errors in LFW. Due to the small number of such errors, the database will be left as is (without corrections) to avoid confusion.
It is important that users of the database provide their algorithms with the database as is, i.e. without correcting the errors below, since previous results published for the database did not have the advantage of correcting for these errors.
Currently, there are five incorrectly labeled matched pairs in View 2. While we do not believe this should have a significant effect on accuracy, we do encourage researchers to be aware of these errors when producing any visualizations (e.g. matched pairs most confidently predicted as mismatched, as the matched pair may actually be mismatched).

勘误表：
如下是LFW库中已知的错误。因为只有少量的几处错误，数据库将保持原样，以防止冲突。这对于使用这个数据库的人来说，非常重要。例如没有修改以下错误，因为先前公布的结果对于修正之处毫无优势。
现在，在视角2里面有5处不正确标注匹配的集合，当我们不相信这将对精度有正向的影响，我们鼓励研发者知晓这些错误，当要产出某些可视化的东西时。 如匹配对很大程度被判断为不匹配，因为匹配对实际上是不匹配的。

The current known errors in View 2 are:
Fold 1: Janica_Kostelic_0001, Janica_Kostelic_0002
Fold 1: Nora_Bendijo_0001, Nora_Bendijo_0002
Fold 5: Jim_OBrien_0001, Jim_OBrien_0002
Fold 5: Jim_OBrien_0001, Jim_OBrien_0003
Fold 5: Elisabeth_Schumacher_0001, Elisabeth_Schumacher_0002
More detail about all the errors is given below.
Note: unless stated otherwise below, any error in a matched pair will mean that the label (“matched”) is wrong. Any error in a mismatched pair, even with the person having the wrong identity, will generally be correct (the label of “mismatched” will still be correct).

在视角2已知错误如下：
组合1： Janica_Kostelic_0001, Janica_Kostelic_0002
组合 1: Nora_Bendijo_0001, Nora_Bendijo_0002
组合 5: Jim_OBrien_0001, Jim_OBrien_0002
组合 5: Jim_OBrien_0001, Jim_OBrien_0003
组合 5: Elisabeth_Schumacher_0001, Elisabeth_Schumacher_0002
更多错误的具体细节如下。
备注： 除非下面另有规定， 任何在匹配对立面的错误，意味标签“匹配”是错误的；任何在不匹配对里的错误，即使是对象的身份弄错，该标签也被认为是正确的。

…..具体的例子

Resources:
Collected resources related to LFW:
Note: We have not verified the accuracy or reliability of the code and data at the following links; we merely provide them as a convenience. Please use your own judgment about the accuracy of the resources below.

资源:
收集了LFW相关的资源：
备注： 我们没有验证以下这些链接的代码数据的真实性，我们仅仅只是便利的提供这些数据。 请用您自己对于以下资源提供的精度，进行判断。

LFWgender
“Getting the known gender based on name of each image in the Labeled Faces in the Wild dataset. This is a python script that calls the genderize.io API with the first name of the person in the image.”

LFWgender
知道LFW库里的每个人的名字，这是调用了一个叫genderize.io API的python脚本，通过名字索引得到用户的性别。

CASIA WebFace Database
“While there are many open source implementations of CNN, none of large scale face dataset is publicly available. The current situation in the field of face recognition is that data is more important than algorithm. To solve this problem, we propose a semi-automatical way to collect face images from Internet and build a large scale dataset containing 10,575 subjects and 494,414 images, called CASIA-WebFace. To the best of our knowledge, the size of this dataset rank second in the literature, only smaller than the private dataset of Facebook (SCF). We encourage those data-consuming methods training on this dataset and reporting performance on LFW. “

CASIA WebFace Database
尽管有许多开源代码实现了CNN，但还没有大规模数据库可以公开获得。 在人脸识别领域是数据比算法更重要。 为了解决这个问题，我们提供半自动的方式，去从互联网上收集人脸图片，建造了一个10575类，494414张图片，即CASIA-Webface. 据我们所知，这个数据库的容量排名第二，只小于Facebook的私有数据库 SCF. 我们鼓励哪些耗费数据的算法，在此数据库上训练，在LFW上报告性能。

LFW3D - collection of frontalized LFW images and Matlab code for frontalization

“Frontalization is the process of synthesizing frontal facing views of faces appearing in single unconstrained photos. Recent reports have suggested that this process may substantially boost the performance of face recognition systems… we explore the simpler approach of using a single, unmodified, 3D surface as an approximation to the shape of all input faces. We show that this leads to a straightforward, efficient and easy to implement method for frontalization. More importantly, it produces aesthetic new frontal views and is surprisingly effective when used for face recognition and gender estimation.”

LFW3D –收集LFW正面的照片，并用Matlab 代码正面化
正面化是对于单张不受限制的照中的人脸，将多张正向人脸合成。 近期的报告指出这个操作能增加人脸识别系统的性能。 我们探索了更简单的方式，用单张，未修改的，3D表面 作为所有输入人脸的最大近似。 我们发现这是简单高效的正面化实现方式。 更重要的是， 它可以产生更多的正向视角，并且用于人脸识别中，异常的高效。

Contact:
Questions and comments can be sent to:
Gary Huang - gbhuang@cs.umass.edu

联系：
有问题和建议，请发送至：
Gary Huang - gbhuang@cs.umass.edu 麻省大学计算机学院

---

-原文参考 LFW官网,

---

总结

以上是生活随笔为你收集整理的对LFW数据库的翻译【1】的全部内容，希望文章能够帮你解决所遇到的问题。

如果觉得生活随笔网站内容还不错，欢迎将生活随笔推荐给好友。