openface/docs/models-and-accuracies.md

# Models and Accuracies
This page overviews different OpenFace neural network models
and is intended for advanced users.

# Model Definitions

The number of parameters are with 128-dimensional embeddings
and do not include the batch normalization running means and
variances.

| Model | Number of Parameters |
|---|---|
| [nn4.small2](https://github.com/cmusatyalab/openface/blob/master/models/openface/nn4.small2.def.lua) | 3733968 |
| [nn4.small1](https://github.com/cmusatyalab/openface/blob/master/models/openface/nn4.small1.def.lua) | 5579520 |
| [nn4](https://github.com/cmusatyalab/openface/blob/master/models/openface/nn4.def.lua) | 6959088 |
| [nn2](https://github.com/cmusatyalab/openface/blob/master/models/openface/nn2.def.lua) | 7472144 |

# Pre-trained Models
Models can be trained in different ways with different datasets.
Pre-trained models are versioned and should be released with
a corresponding model definition.
Switch between models with caution because the embeddings
not compatible with each other.

The current models are trained with a combination of 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).

The models can be downloaded from our storage servers:

+ [nn4.v1](http://openface-models.storage.cmusatyalab.org/nn4.v1.t7)
+ [nn4.v2](http://openface-models.storage.cmusatyalab.org/nn4.v2.t7)
+ [nn4.small1.v1](http://openface-models.storage.cmusatyalab.org/nn4.small1.v1.t7)
+ [nn4.small2.v1](http://openface-models.storage.cmusatyalab.org/nn4.small2.v1.t7)

API differences between the models are:

| Model  | alignment `landmarkIndices` |
|---|---|
| nn4.v1 | `openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP` |
| nn4.v2 | `openface.AlignDlib.OUTER_EYES_AND_NOSE` |
| nn4.small1.v1 | `openface.AlignDlib.OUTER_EYES_AND_NOSE` |
| nn4.small2.v1 | `openface.AlignDlib.OUTER_EYES_AND_NOSE` |

## Performance
The performance is measured by averaging 500 forward passes with
[util/profile-network.lua](https://github.com/cmusatyalab/openface/blob/master/util/profile-network.lua)
and the following results use OpenBLAS on an 8 core 3.70 GHz CPU
and a Tesla K40 GPU.

| Model  | Runtime (CPU) | Runtime (GPU) |
|---|---|---|
| nn4.v1 | 75.67 ms ± 19.97 ms | 21.96 ms ± 6.71 ms |
| nn4.v2 | 82.74 ms ± 19.96 ms | 20.82 ms ± 6.03 ms |
| nn4.small1.v1 | 69.58 ms ± 16.17 ms | 15.90 ms ± 5.18 ms |
| nn4.small2.v1 | 58.9 ms ± 15.36 ms | 13.72 ms ± 4.64 ms |

## Accuracy on the LFW Benchmark

Even though the public datasets we trained on have orders of magnitude less data
than private industry datasets, the accuracy is remarkably high
on the standard
[LFW](http://vis-www.cs.umass.edu/lfw/results.html)
benchmark.
We had to fallback to using the deep funneled versions for
58 of 13233 images because dlib failed to detect a face or landmarks.

| Model | Accuracy | AUC |
|---|---|---|
| **nn4.small2.v1** (Default) | 0.9292 ± 0.0134 | 0.973 |
| nn4.small1.v1 | 0.9210 ± 0.0160 | 0.973 |
| nn4.v2 | 0.9157 ± 0.0152 | 0.966 |
| nn4.v1 | 0.7612 ± 0.0189 | 0.853 |
| FaceNet Paper (Reference) | 0.9963 ± 0.009 | not provided |

### ROC Curves

#### nn4.small2.v1
![](https://raw.githubusercontent.com/cmusatyalab/openface/master/evaluation/lfw.nn4.small2.v1/roc.png)

#### nn4.small1.v1
![](https://raw.githubusercontent.com/cmusatyalab/openface/master/evaluation/lfw.nn4.small1.v1/roc.png)

#### nn4.v2
![](https://raw.githubusercontent.com/cmusatyalab/openface/master/evaluation/lfw.nn4.v2/roc.png)

#### nn4.v1
![](https://raw.githubusercontent.com/cmusatyalab/openface/master/evaluation/lfw.nn4.v1/roc.png)


## Running The LFW Experiment

This can be generated with the following commands from the root `openface`
directory, assuming you have downloaded and placed the raw and
[deep funneled](http://vis-www.cs.umass.edu/deep_funnel.html)
LFW data from [here](http://vis-www.cs.umass.edu/lfw/)
in `./data/lfw/raw` and `./data/lfw/deepfunneled`.
Also asve [pairs.txt](http://vis-www.cs.umass.edu/lfw/pairs.txt) in
`./data/lfw/pairs.txt`.

1. Install prerequisites as below.
2. Preprocess the raw `lfw` images, change `8` to however many
   separate processes you want to run:
   `for N in {1..8}; do ./util/align-dlib.py data/lfw/raw align outerEyesAndNose data/lfw/dlib-affine-sz:96 --size 96 & done`.
   Fallback to deep funneled versions for images that dlib failed
   to align:
   `./util/align-dlib.py data/lfw/raw align outerEyesAndNose data/lfw/dlib-affine-sz:96 --size 96 --fallbackLfw data/lfw/deepfunneled`
3. Generate representations with `./batch-represent/main.lua -outDir evaluation/lfw.nn4.small2.v1.reps -model models/openface/nn4.small2.v1.t7 -data data/lfw/dlib-affine-sz:96`
4. Generate the ROC curve from the `evaluation` directory with `./lfw.py nn4.small2.v1 lfw.nn4.small2.v1.reps`.
   This creates `roc.pdf` in the `lfw.nn4.small2.v1.reps` directory.

# Projects with Higher Accuracy

If you're interested in higher accuracy open source code, see:

## [Oxford's VGG Face Descriptor](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/)

This 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,
which makes tasks like classification and clustering more difficult.
Their triplet model hasn't yet been released, but will provide
embeddings similar to FaceNet.
The triplet model will be supported by OpenFace once it's released.

## [Deep Face Representation](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.