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https://github.com/rcmalli/keras-vggface
VGGFace implementation with Keras Framework
https://github.com/rcmalli/keras-vggface
keras tensorflow vggface
Last synced: 11 days ago
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VGGFace implementation with Keras Framework
- Host: GitHub
- URL: https://github.com/rcmalli/keras-vggface
- Owner: rcmalli
- License: mit
- Created: 2016-10-17T15:07:40.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2024-07-09T14:48:05.000Z (4 months ago)
- Last Synced: 2024-10-25T13:47:38.749Z (16 days ago)
- Topics: keras, tensorflow, vggface
- Language: Python
- Size: 5.89 MB
- Stars: 934
- Watchers: 38
- Forks: 418
- Open Issues: 40
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE.txt
Awesome Lists containing this project
README
# keras-vggface [](https://travis-ci.org/rcmalli/keras-vggface) [](https://badge.fury.io/py/keras-vggface) [](https://pepy.tech/project/keras-vggface)
Oxford VGGFace Implementation using Keras Functional Framework v2+
- Models are converted from original caffe networks.
- It supports only Tensorflow backend.
- You can also load only feature extraction layers with VGGFace(include_top=False) initiation.
- When you use it for the first time , weights are downloaded and stored in ~/.keras/models/vggface folder.
- If you don't know where to start check the [blog posts](https://github.com/rcmalli/keras-vggface#projects--blog-posts) that are using this library.
~~~bash
# Most Recent One (Suggested)
pip install git+https://github.com/rcmalli/keras-vggface.git
# Release Version
pip install keras_vggface
~~~
### Library Versions
- Keras v2.2.4
- Tensorflow v1.14.0
- **Warning: Theano backend is not supported/tested for now.**
### Example Usage
#### Available Models
```python
from keras_vggface.vggface import VGGFace
# Based on VGG16 architecture -> old paper(2015)
vggface = VGGFace(model='vgg16') # or VGGFace() as default
# Based on RESNET50 architecture -> new paper(2017)
vggface = VGGFace(model='resnet50')
# Based on SENET50 architecture -> new paper(2017)
vggface = VGGFace(model='senet50')
```
#### Feature Extraction
- Convolution Features
```python
from keras.engine import Model
from keras.layers import Input
from keras_vggface.vggface import VGGFace
# Convolution Features
vgg_features = VGGFace(include_top=False, input_shape=(224, 224, 3), pooling='avg') # pooling: None, avg or max
# After this point you can use your model to predict.
# ...
```
- Specific Layer Features
```python
from keras.engine import Model
from keras.layers import Input
from keras_vggface.vggface import VGGFace
# Layer Features
layer_name = 'layer_name' # edit this line
vgg_model = VGGFace() # pooling: None, avg or max
out = vgg_model.get_layer(layer_name).output
vgg_model_new = Model(vgg_model.input, out)
# After this point you can use your model to predict.
# ...
```
#### Finetuning
- VGG16
```python
from keras.engine import Model
from keras.layers import Flatten, Dense, Input
from keras_vggface.vggface import VGGFace
#custom parameters
nb_class = 2
hidden_dim = 512
vgg_model = VGGFace(include_top=False, input_shape=(224, 224, 3))
last_layer = vgg_model.get_layer('pool5').output
x = Flatten(name='flatten')(last_layer)
x = Dense(hidden_dim, activation='relu', name='fc6')(x)
x = Dense(hidden_dim, activation='relu', name='fc7')(x)
out = Dense(nb_class, activation='softmax', name='fc8')(x)
custom_vgg_model = Model(vgg_model.input, out)
# Train your model as usual.
# ...
```
- RESNET50 or SENET50
```python
from keras.engine import Model
from keras.layers import Flatten, Dense, Input
from keras_vggface.vggface import VGGFace
#custom parameters
nb_class = 2
vgg_model = VGGFace(include_top=False, input_shape=(224, 224, 3))
last_layer = vgg_model.get_layer('avg_pool').output
x = Flatten(name='flatten')(last_layer)
out = Dense(nb_class, activation='softmax', name='classifier')(x)
custom_vgg_model = Model(vgg_model.input, out)
# Train your model as usual.
# ...
```
#### Prediction
- Use `utils.preprocess_input(x, version=1)` for VGG16
- Use `utils.preprocess_input(x, version=2)` for RESNET50 or SENET50
```python
import numpy as np
from keras.preprocessing import image
from keras_vggface.vggface import VGGFace
from keras_vggface import utils
# tensorflow
model = VGGFace() # default : VGG16 , you can use model='resnet50' or 'senet50'
# Change the image path with yours.
img = image.load_img('../image/ajb.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_input(x, version=1) # or version=2
preds = model.predict(x)
print('Predicted:', utils.decode_predictions(preds))
```
### References
- [Keras Framework](www.keras.io)
- [Oxford VGGFace Website](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/)
- [Related Paper 1](http://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf)
- [Related Paper 2](http://www.robots.ox.ac.uk/~vgg/publications/2018/Cao18/cao18.pdf)
### Licence
- Check Oxford Webpage for the license of the original models.
- The code that provided in this project is under MIT License.
### Projects / Blog Posts
If you find this project useful, please include reference link in your work. You can create PR's to this document with your project/blog link.
- [Live Face Identification with pre-trained VGGFace2 model](https://www.dlology.com/blog/live-face-identification-with-pre-trained-vggface2-model/)
- [How to Perform Face Recognition With VGGFace2 in Keras](https://machinelearningmastery.com/how-to-perform-face-recognition-with-vggface2-convolutional-neural-network-in-keras/)
- [An extremely small FaceRecog project for extreme beginners, and a few thoughts on the future](https://kevincodeidea.wordpress.com/2020/01/14/an-extremely-small-facerecog-project-for-extreme-beginners-and-a-few-thoughts-on-future-part-ii-transfer-learning-and-keras/)