All Projects → fwang91 → Imdb Face

fwang91 / Imdb Face

A new large-scale noise-controlled face recognition dataset.

Projects that are alternatives of or similar to Imdb Face

Awesome machine learning solutions
A curated list of repositories for my book Machine Learning Solutions.
Stars: ✭ 65 (-83.71%)
Mutual labels:  dataset, face-recognition
Awesome Face
😎 face releated algorithm, dataset and paper
Stars: ✭ 739 (+85.21%)
Mutual labels:  dataset, face-recognition
MaskedFaceRepresentation
Masked face recognition focuses on identifying people using their facial features while they are wearing masks. We introduce benchmarks on face verification based on masked face images for the development of COVID-safe protocols in airports.
Stars: ✭ 17 (-95.74%)
Mutual labels:  dataset, face-recognition
Facerank
FaceRank - Rank Face by CNN Model based on TensorFlow (add keras version). FaceRank-人脸打分基于 TensorFlow (新增 Keras 版本) 的 CNN 模型(QQ群:167122861)。技术支持:http://tensorflow123.com
Stars: ✭ 841 (+110.78%)
Mutual labels:  dataset, face-recognition
Maskedface Net
MaskedFace-Net is a dataset of human faces with a correctly and incorrectly worn mask based on the dataset Flickr-Faces-HQ (FFHQ).
Stars: ✭ 152 (-61.9%)
Mutual labels:  dataset, face-recognition
Masktheface
Convert face dataset to masked dataset
Stars: ✭ 167 (-58.15%)
Mutual labels:  dataset, face-recognition
Meglass
An eyeglass face dataset collected and cleaned for face recognition evaluation, CCBR 2018.
Stars: ✭ 281 (-29.57%)
Mutual labels:  dataset, face-recognition
Medmnist
[ISBI'21] MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
Stars: ✭ 338 (-15.29%)
Mutual labels:  dataset
Curated List Of Awesome 3d Morphable Model Software And Data
The idea of this list is to collect shared data and algorithms around 3D Morphable Models. You are invited to contribute to this list by adding a pull request. The original list arised from the Dagstuhl seminar on 3D Morphable Models https://www.dagstuhl.de/19102 in March 2019.
Stars: ✭ 375 (-6.02%)
Mutual labels:  face-recognition
Dsprites Dataset
Dataset to assess the disentanglement properties of unsupervised learning methods
Stars: ✭ 340 (-14.79%)
Mutual labels:  dataset
Pcam
The PatchCamelyon (PCam) deep learning classification benchmark.
Stars: ✭ 340 (-14.79%)
Mutual labels:  dataset
Face Recognition
Deep face recognition with Keras, Dlib and OpenCV
Stars: ✭ 342 (-14.29%)
Mutual labels:  face-recognition
Tfrecord
TFRecord reader for PyTorch
Stars: ✭ 377 (-5.51%)
Mutual labels:  dataset
Libfacerec
Face Recognition Library for OpenCV.
Stars: ✭ 341 (-14.54%)
Mutual labels:  face-recognition
Cdp
Code for our ECCV 2018 work.
Stars: ✭ 391 (-2.01%)
Mutual labels:  face-recognition
Eseur Code Data
Code and data used to create the examples in "Evidence-based Software Engineering based on the publicly available data"
Stars: ✭ 340 (-14.79%)
Mutual labels:  dataset
Free Spoken Digit Dataset
A free audio dataset of spoken digits. Think MNIST for audio.
Stars: ✭ 396 (-0.75%)
Mutual labels:  dataset
Cmu Multimodalsdk
CMU MultimodalSDK is a machine learning platform for development of advanced multimodal models as well as easily accessing and processing multimodal datasets.
Stars: ✭ 388 (-2.76%)
Mutual labels:  dataset
Trashnet
Dataset of images of trash; Torch-based CNN for garbage image classification
Stars: ✭ 368 (-7.77%)
Mutual labels:  dataset
Data
Python related videos and metadata powering =>
Stars: ✭ 355 (-11.03%)
Mutual labels:  dataset

The Devil of Face Recognition is in the Noise(ECCV'18)

By Fei Wang, Liren Chen, Cheng Li, Shiyao Huang, Yanjie Chen, Chen Qian, Chen Change Loy

imdbface

IMDb-Face is a new large-scale noise-controlled dataset for face recognition research. The dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from 2.0 million raw images. All images are obtained from the IMDb website. A detailed introduction of IMDb-Face can be found in the paper(https://arxiv.org/abs/1807.11649).

We hope that the IMDb-Face dataset could shed lights on the influences of data noise to the face recognition task, and point to potential labelling strategies to mitigate some of the problems. It could serve as a relatively clean data to facilitate future studies of noises in large-scale face recognition.

Citation

If you find IMDb-Face useful in your research, please cite:

@article{wang2018devil,
	title={The Devil of Face Recognition is in the Noise},
	author={Wang, Fei and Chen, Liren and Li, Cheng and Huang, Shiyao and Chen, Yanjie and Qian, Chen and Loy, Chen Change},
	journal={arXiv preprint arXiv:1807.11649},
	year={2018}
}

Contents

  1. Data Download
  2. Data Statistics
  3. Overlap with Face Recognition Benchmarks
  4. Notation
  5. Contact

Data Download

IMDb-Face.csv

GoogleDrive Download: https://drive.google.com/open?id=134kOnRcJgHZ2eREu8QRi99qj996Ap_ML

BaiduDrive Download: https://pan.baidu.com/s/1eRylM-jMgjYL6cyU6qQd8g

Note: We found that the resolution of some images has changed, so we provide the shape information of each image. If the resolution of the newly downloaded image is not the same as the one we provide, you can rescale the rectangle and get the final rectangle information.

Data Statistics

Overall

Total number of images: 1.7M

Total number of identities: 59k

IMDb-Face dataset statistics dataset

Overlap with Face Recognition Benchmarks

We have removed celebrity images of which the identification appear in the LFW dataset, Facescrub (MegaFace evaluation images) and YTF based on names. You can evaluate a face recognition model trained on IMDb-Face on these public benchmarks directly.

Notation

(1) IMDb-Face does not own the copyright of the images. IMDb-Face only provides URLs of images. The images in their original resolutions may be subject to copyright, so we cannot make them publicly available on our server. The dataset is released for non-commercial research and/or educational purposes.

(2) If you are the celebrity included in the IMDb-Face and you do not want to be included in the dataset, please contact us and we will remove the data based on your request.

Contact

Fei Wang

Questions can also be left as issues in the repository.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].