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Docs: Update links to other projects in 'accuracy'

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Brandon Amos 2015-12-02 03:28:53 -05:00
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@ -33,19 +33,22 @@ in `./data/lfw/raw` and `./data/lfw/deepfunneled`.
If you're interested in higher accuracy open source code, see: If you're interested in higher accuracy open source code, see:
1. [Oxford's VGG Face Descriptor](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/), ## [Oxford's VGG Face Descriptor](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/)
which is licensed for non-commercial research purposes.
They've released their softmax network, which obtains .9727 accuracy
on the LFW and will release their triplet network (0.9913 accuracy)
and data soon.
Their softmax model doesn't embed features like FaceNet, This is licensed for non-commercial research purposes.
which makes tasks like classification and clustering more difficult. They've released their softmax network, which obtains .9727 accuracy
Their triplet model hasn't yet been released, but will provide on the LFW and will release their triplet network (0.9913 accuracy)
embeddings similar to FaceNet. and data soon (?).
The triplet model will be supported by OpenFace once it's released.
2. [AlfredXiangWu/face_verification_experiment](https://github.com/AlfredXiangWu/face_verification_experiment), Their softmax model doesn't embed features like FaceNet,
which uses Caffe and doesn't yet have a license. which makes tasks like classification and clustering more difficult.
The accuracy on the LFW is .9777. Their triplet model hasn't yet been released, but will provide
This model doesn't embed features like FaceNet, embeddings similar to FaceNet.
which makes tasks like classification and clustering more difficult. The triplet model will be supported by OpenFace once it's released.
## [AlfredXiangWu/face_verification_experiment](https://github.com/AlfredXiangWu/face_verification_experiment)
This uses Caffe and doesn't yet have a license.
The accuracy on the LFW is .9777.
This model doesn't embed features like FaceNet,
which makes tasks like classification and clustering more difficult.