tf.app.flags.DEFINE_string('train_path', 'E://dataset//vggface2//train', 'Filepattern for training data.') # 测试数据路径 tf.app.flags.DEFINE_string('test_path', 'E://dataset//vggface2//test', 'Filepattern for testing data.') tf.app.flags.DEFINE_string('model_path', 'modeldir....
4. Dataset Collection 数据集建立的过程,对应一般 paper 的 method 部分。 一般包含几个阶段: 数据来源 具体的收集举措 去除噪声 5. Experiment 实验部分又是一个重点,关键在于说明自己数据集的优势。 收集人脸在不同 pose, age 下的照片,用以验证不同模型 pose/age invariant 的能力 在VGGFace2 和其他数据集上...
Zisserman, "Vg- gface2: A dataset for recognising faces across pose and age," in FG, 2018.Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, "Vggface2: A dataset for recognising faces across pose and age," in IEEE International Conference on Automatic Face & Gesture ...
III.VGGFACE2的概述 A.Dataset统计 VGGFace2数据集包含来自9131名名人的330万张图片,这些名人跨越多种族,例如它包括比VGGFace更多的中国和印度面孔(尽管,种族平衡仍然受到名人和公众人物分布的限制)和职业(例如政治家和运动员)。图像是从谷歌图像搜索下载的,并显示姿势,年龄,灯光和背景的大变化。该数据集大致具有性别...
The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimize the label noise. We describe how the dataset was ...
VGGFace2 是一个大规模的人脸识别数据集,包含 9131 个人的面部。 图像从 Google 图片搜索下载,在姿势,年龄,照明,种族和职业方面有很大差异。该数据集于 2015 年由牛津大学工程科学系视觉几何组发布,相关论文为 Deep Face Recognition。 VGGFace2 是一个大规模人脸识别数据,包含331万图片,9131个ID,平均图片个数为36...
VGGFace2: A dataset for recognising faces across pose and age, Q. Cao, L. Shen, W. Xie, O. M. Parkhi, A. Zisserman, In FG 2018. News DateUpdate 2018-10-01Models imported to PyTorch. Example scripts for cropping faces and evaluating on IJB-B can be found in the folder 'standard...
VGG-Face dataset, described in [2], is not planned to be supported in this repo. If you are interested in models for VGG-Face, seekeras-vggface. References ZQ. Cao, L. Shen, W. Xie, O. M. Parkhi, A. Zisserman, VGGFace2: A dataset for recognising faces across pose and age, 20...
VGGface2是一个能够用于识别不同姿态和年龄人脸的数据集,数据集包含了440028张有效图片,数据集内人脸数据已经对齐 - 飞桨AI Studio
be evaluated using the same type of image standardization. Hence, the flag--use_fixed_image_standardizationshould be used also for evaluation. 1% of the training images are used for validation. Since the amount of label noise in the VGGFace2 dataset is low no dataset filtering has been ...