107 lines
3.7 KiB
Markdown
107 lines
3.7 KiB
Markdown
# Demo 3: Training a Classifier
|
||
OpenFace's core provides a feature extraction method to
|
||
obtain a low-dimensional representation of any face.
|
||
[demos/classifier.py](https://github.com/cmusatyalab/openface/blob/master/demos/classifier.py)
|
||
shows a demo of how these representations can be
|
||
used to create a face classifier.
|
||
|
||
There is a distinction between training the deep neural network (DNN)
|
||
model for feature representation
|
||
and training a model for classifying people with the DNN model.
|
||
This shows how to use a pre-trained DNN model to train and use
|
||
a classification model.
|
||
|
||
## Creating a Classification Model
|
||
|
||
### 1. Create raw image directory.
|
||
Create a directory for your raw images so that images from different
|
||
people are in different subdirectories. The names of the labels or
|
||
images do not matter, and each person can have a different amount of images.
|
||
The images should be formatted as `jpg` or `png` and have
|
||
a lowercase extension.
|
||
|
||
```
|
||
$ tree data/mydataset/raw
|
||
person-1
|
||
├── image-1.jpg
|
||
├── image-2.png
|
||
...
|
||
└── image-p.png
|
||
|
||
...
|
||
|
||
person-m
|
||
├── image-1.png
|
||
├── image-2.jpg
|
||
...
|
||
└── image-q.png
|
||
```
|
||
|
||
|
||
### 2. Preprocess the raw images
|
||
Change `8` to however many
|
||
separate processes you want to run:
|
||
`for N in {1..8}; do ./util/align-dlib.py <path-to-raw-data> align innerEyesAndBottomLip <path-to-aligned-data> --size 96 & done`.
|
||
|
||
### 3. Generate Representations
|
||
`./batch-represent/main.lua -outDir <feature-directory> -data <path-to-aligned-data>`
|
||
creates `reps.csv` and `labels.csv` in `<feature-directory>`.
|
||
|
||
### 4. Create the Classification Model
|
||
Use `./demos/classifier.py train <feature-directory>` to produce
|
||
the classification model which is an SVM saved to disk as
|
||
a Python pickle.
|
||
|
||
Training uses [scikit-learn](http://scikit-learn.org) to perform
|
||
a grid search over SVM parameters.
|
||
For 1000's of images, training the SVMs takes seconds.
|
||
|
||
## Classifying New Images
|
||
We have released a `celeb-classifier.nn4.small2.v1.pkl` classification model
|
||
that is trained on about 6000 total images of the following people,
|
||
which are the people with the most images in our dataset.
|
||
Classifiers can be created with far less images per
|
||
person.
|
||
|
||
+ America Ferrera
|
||
+ Amy Adams
|
||
+ Anne Hathaway
|
||
+ Ben Stiller
|
||
+ Bradley Cooper
|
||
+ David Boreanaz
|
||
+ Emily Deschanel
|
||
+ Eva Longoria
|
||
+ Jon Hamm
|
||
+ Steve Carell
|
||
|
||
For an example, consider the following small set of images
|
||
the model has no knowledge of.
|
||
For an unknown person, a prediction still needs to be made, but
|
||
the confidence score is usually lower.
|
||
|
||
Run the classifier with:
|
||
|
||
```
|
||
./demos/classifier.py infer ./models/openface/celeb-classifier.nn4.small2.v1.pkl images/examples/{carell,adams,lennon}*
|
||
```
|
||
|
||
| Person | Image | Prediction | Confidence |
|
||
|---|---|---|---|
|
||
| Carell | <img src='https://raw.githubusercontent.com/cmusatyalab/openface/master/images/examples/carell.jpg' width='200px'></img> | SteveCarell | 0.97 |
|
||
| Adams | <img src='https://raw.githubusercontent.com/cmusatyalab/openface/master/images/examples/adams.jpg' width='200px'></img> | AmyAdams | 0.81 |
|
||
| Lennon 1 (Unknown) | <img src='https://raw.githubusercontent.com/cmusatyalab/openface/master/images/examples/lennon-1.jpg' width='200px'></img> | SteveCarell | 0.50 |
|
||
| Lennon 2 (Unknown) | <img src='https://raw.githubusercontent.com/cmusatyalab/openface/master/images/examples/lennon-2.jpg' width='200px'></img> | DavidBoreanaz | 0.43 |
|
||
|
||
# Minimal Working Example to Extract Features
|
||
|
||
```
|
||
mkdir -p classify-test/raw/{lennon,clapton}
|
||
cp images/examples/lennon-* classify-test/raw/lennon
|
||
cp images/examples/clapton-* classify-test/raw/clapton
|
||
./util/align-dlib.py classify-test/raw align innerEyesAndBottomLip classify-test/aligned --size 96
|
||
./batch-represent/main.lua -outDir classify-test/features -data classify-test/aligned
|
||
...
|
||
nImgs: 4
|
||
Represent: 4/4
|
||
```
|