DeepTuner is an open source Python package for fine-tuning computer vision (CV) based deep models. It supports multiple architectures including Siamese Networks with triplet loss and ArcFace with additive angular margin loss. The package provides various model backbones, data preprocessing tools, and evaluation metrics.
To install the package, use the following command:
pip install DeepTunerDeepTuner now supports ArcFace training with additive angular margin loss, which is particularly effective for face recognition tasks:
from deeptuner.backbones.resnet import ResNetBackbone
from deeptuner.losses.arcface_loss import ArcFaceModel, arcface_loss
from deeptuner.datagenerators.arcface_generator import ArcFaceDataGenerator
# Create data generators
train_generator = ArcFaceDataGenerator(
data_dir='path/to/train/data',
batch_size=32,
image_size=(224, 224),
augment=True
)
# Create backbone and ArcFace model
backbone = ResNetBackbone(input_shape=(224, 224, 3))
backbone_model = backbone.create_model()
model = ArcFaceModel(
backbone=backbone_model,
num_classes=num_classes,
embedding_dim=512,
margin=0.5,
scale=64.0
)
# Compile and train
model.compile(
optimizer='adam',
loss=arcface_loss(),
metrics=['accuracy']
)
model.fit(
train_generator.create_dataset(is_training=True),
epochs=50
)For similarity learning tasks, you can use the Siamese architecture with triplet loss:
from deeptuner.backbones.resnet import ResNetBackbone
from deeptuner.architectures.siamese import SiameseArchitecture
from deeptuner.losses.triplet_loss import triplet_loss
from deeptuner.datagenerators.triplet_data_generator import TripletDataGenerator
# Create data generators
train_generator = TripletDataGenerator(
train_paths, train_labels,
batch_size=32,
image_size=(224, 224),
num_classes=num_classes
)
# Create Siamese network
backbone = ResNetBackbone(input_shape=(224, 224, 3))
embedding_model = backbone.create_model()
siamese_architecture = SiameseArchitecture(
input_shape=(224, 224, 3),
embedding_model=embedding_model
)
siamese_network = siamese_architecture.create_siamese_network()
# Train the model
model.compile(optimizer='adam', loss=triplet_loss(margin=0.5))
model.fit(train_generator, epochs=50)- Multiple architectures:
- Siamese Networks with triplet loss
- ArcFace with additive angular margin loss
- Various backbone models:
- ResNet
- EfficientNet
- MobileNet
- Specialized data generators:
- TripletDataGenerator for Siamese networks
- ArcFaceDataGenerator for ArcFace training
- Training utilities:
- Fine-tuning callbacks
- Learning rate scheduling
- Wandb integration for experiment tracking
You can use a configuration file (e.g., JSON) to store hyperparameters. Example config for ArcFace:
{
"data_dir": "path/to/data",
"image_size": [224, 224],
"batch_size": 32,
"epochs": 50,
"learning_rate": 0.001,
"patience": 5,
"unfreeze_layers": 10,
"embedding_dim": 512,
"arcface_margin": 0.5,
"arcface_scale": 64.0
}Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.