38 lines
1.7 KiB
Markdown
38 lines
1.7 KiB
Markdown
# Model Definitions
|
|
Model definitions should be kept in [models/openface](https://github.com/cmusatyalab/openface/blob/master/models/openface),
|
|
where we have provided definitions of the [NN2](https://github.com/cmusatyalab/openface/blob/master/models/openface/nn2.def.lua)
|
|
and [nn4](https://github.com/cmusatyalab/openface/blob/master/models/openface/nn4.def.lua) as described in the paper,
|
|
but with batch normalization and no normalization in the lower layers.
|
|
The inception layers are introduced in
|
|
[Going Deeper with Convolutions](http://arxiv.org/abs/1409.4842)
|
|
by Christian Szegedy et al.
|
|
|
|
# Pre-trained Models
|
|
Pre-trained models are versioned and should be released with
|
|
a corresponding model definition.
|
|
We currently only provide a pre-trained model for `nn4.v1`
|
|
because we have limited access to large-scale face recognition
|
|
datasets.
|
|
|
|
## nn4.v1
|
|
This model has been trained by combining the two largest (of August 2015)
|
|
publicly-available face recognition datasets based on names:
|
|
[FaceScrub](http://vintage.winklerbros.net/facescrub.html)
|
|
and [CASIA-WebFace](http://arxiv.org/abs/1411.7923).
|
|
This model was trained for about 300 hours on a Tesla K40 GPU.
|
|
|
|
The following plot shows the triplet loss on the training
|
|
and test set.
|
|
Each training epoch is defined to be 1000 minibatches, where
|
|
each minibatch processes 100 triplets.
|
|
Each testing epoch is defined to be 300 minibatches,
|
|
where each minibatch processes 100 triplets.
|
|
Semi-hard triplets are used on the training set, and
|
|
random triplets are used on the testing set.
|
|
Our `nn4.v1` model is from epoch 177.
|
|
|
|
The LFW section above shows that this model obtains a mean
|
|
accuracy of 0.8483 ± 0.0172 with an AUC of 0.923.
|
|
|
|
|