https://github.com/4uiiurz1/keras-arcface
Keras implementation of ArcFace, CosFace, and SphereFace
https://github.com/4uiiurz1/keras-arcface
arcface deep-learning keras
Last synced: 6 months ago
JSON representation
Keras implementation of ArcFace, CosFace, and SphereFace
- Host: GitHub
- URL: https://github.com/4uiiurz1/keras-arcface
- Owner: 4uiiurz1
- License: mit
- Created: 2019-04-17T11:43:15.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-05-09T02:02:47.000Z (about 4 years ago)
- Last Synced: 2024-06-29T09:35:12.739Z (almost 2 years ago)
- Topics: arcface, deep-learning, keras
- Language: Python
- Homepage:
- Size: 442 KB
- Stars: 281
- Watchers: 11
- Forks: 65
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-deep-learning - keras-arcface
README
# Keras implementation of ArcFace, CosFace, and SphereFace
This repository contains code for **ArcFace**, **CosFace**, and **SphereFace** based on [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/abs/1801.07698) implemented in Keras.
## Requirements
- Python 3.6
- Keras 2.2.4
## Usage
### Train
```python
input = Input(shape=(28, 28, 1))
label = Input(shape=(10,))
x = Conv2D(32, kernel_size=(3, 3), activation='relu')(input)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Conv2D(64, kernel_size=(3, 3), activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
x = Flatten()(x)
x = Dense(512, kernel_initializer='he_normal')(x)
x = BatchNormalization()(x)
output = ArcFace(num_classes=10)([x, label])
model = Model([input, label], output)
model.compile(loss='categorical_crossentropy',
optimizer=Adam(),
metrics=['accuracy'])
model.fit([x_train, y_train],
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=([x_test, y_test], y_test),
callbacks=[ModelCheckpoint('model.hdf5',
verbose=1, save_best_only=True)])
```
### Test
```python
model.load_weights('model.hdf5')
model = Model(inputs=model.input[0], outputs=model.layers[-3].output)
embedded_features = model.predict(x_test, verbose=1)
embedded_features /= np.linalg.norm(embedded_features, axis=1, keepdims=True)
```
## Training
### MNIST
ArcFace:
```
python train.py --arch vgg8_arcface
```
CosFace:
```
python train.py --arch vgg8_cosface
```
SphereFace:
```
python train.py --arch vgg8_sphereface
```
## Results
### MNIST
