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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Face recognition using Tensorflow
| Date | Stars |
|---|---|
| 2026-07-24 | 14340 |
| 2026-07-25 | 14341 |
| 2026-07-28 | 14341 |
| 2026-07-30 | 14341 |
| 2026-08-06 | 14375 |
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+34 stars today
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# Face Recognition using Tensorflow [![Build Status][travis-image]][travis] [travis-image]: http://travis-ci.org/davidsandberg/facenet.svg?branch=master [travis]: http://travis-ci.org/davidsandberg/facenet This is a TensorFlow implementation of the face recognizer described in the paper ["FaceNet: A Unified Embedding for Face Recognition and Clustering"](http://arxiv.org/abs/1503.03832). The project also uses ideas from the paper ["Deep Face Recognition"](http://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf) from the [Visual Geometry Group](http://www.robots.ox.ac.uk/~vgg/) at Oxford. ## Compatibility The code is tested using Tensorflow r1.7 under Ubuntu 14.04 with Python 2.7 and Python 3.5. The test cases can be found [here](https://github.com/davidsandberg/facenet/tree/master/test) and the results can be found [here](http://travis-ci.org/davidsandberg/facenet). ## News | Date | Update | |----------|--------| | 2018-04-10 | Added new models trained on Casia-WebFace and VGGFace2 (see below). Note that the models uses fixed image standardization (see [wiki](https://github.com/davidsandberg/facenet/wiki/Training-using-the-VGGFace2-dataset)). | | 2018-03-31 | Added a new, more flexible input pipeline as well as a bunch of minor updates. | | 2017-05-13 | Removed a bunch of older non-slim models. Moved the last bottleneck layer into the respective models. Corrected normalization of Center Loss. | | 2017-05-06 | Added code to [train a classifier on your own images](https://github.com/davidsandberg/facenet/wiki/Train-a-classifier-on-own-images). Renamed facenet_train.py to train_tripletloss.py and facenet_train_classifier.py to train_softmax.py. | | 2017-03-02 | Added pretrained models that generate 128-dimensional embeddings.| | 2017-02-22 | Updated to Tensorflow r1.0. Added Continuous Integration using Travis-CI.| | 2017-02-03 | Added models where only trainable variables has been stored in the checkpoint. These are therefore significantly smaller. | | 2017-01-27 | Added a model trained on a subset of the MS-Celeb-1M dataset. The LFW accuracy of this model is around 0.994. | | 2017‑01‑02 | Updated to run with Tensorflow r0.12. Not sure if it runs with older versions of Tensorflow though. | ## Pre-trained models | Model name | LFW accuracy | Training dataset | Architecture | |-----------------|--------------|------------------|-------------| | [20180408-102900](https://drive.google.com/open?id=1R77HmFADxe87GmoLwzfgMu_HY0IhcyBz) | 0.9905 | CASIA-WebFace | [Inception ResNet v1](https://github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py) | | [20180402-114759](https://drive.google.com/open?id=1EXPBSXwTaqrSC0OhUdXNmKSh9qJUQ55-) | 0.9965 | VGGFace2 | [Inception ResNet v1](https://github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py) | NOTE: If you use any of the models, please do not forget to give proper credit to those providing the training dataset as well. ## Inspiration The code is heavily inspired by the [OpenFace](https://github.com/cmusatyalab/openface) implementation. ## Training data The [CASIA-WebFace](http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html) dataset has been used for training. This training set consists of total of 453 453 images over 10 575 identities after face detection. Some performance improvement has been seen if the dataset has been filtered before training. Some more information about how this was done will come later. The best performing model has been trained on the [VGGFace2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) dataset consisting of ~3.3M faces and ~9000 classes. ## Pre-processing ### Face alignment using MTCNN One problem with the above approach seems to be that the Dlib face detector misses some of the hard examples (partial occlusion, silhouettes, etc). This makes the training set too "easy" which causes the model to perform worse on other benchmarks. T
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:de6895720513a012, topic:computer-vision, topic:face-recognition, desc:face recognition
matched fp:de6895720513a012, topic:deep-learning, topic:tensorflow