1.CalebA人脸数据集（官网链接）是香港中文大学的开放数据，包含10,177个名人身份的202,599张人脸图片，并且都做好了特征标记，这对人脸相关的训练是非常好用的数据集。
2.WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% data as training, validation and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets, we do not release bounding box ground truth for the test images. Users are required to submit final prediction files, which we shall proceed to evaluate.
3.IJB-A dataset: IJB-A is proposed for face detection and face recognition. IJB-A contains 24,327 images and 49,759 faces.
4.MALF dataset: MALF is the first face detection dataset that supports fine-gained evaluation. MALF consists of 5,250 images and 11,931 faces.
5.FDDB dataset: FDDB dataset contains the annotations for 5,171 faces in a set of 2,845 images.
6.AFW dataset: AFW dataset is built using Flickr images. It has 205 images with 473 labeled faces. For each face, annotations include a rectangular bounding box, 6 landmarks and the pose angles.
7.数据堂，大概有8.3万张人脸标注样本。