This python library is useful for creating deep learning models, supporting Classification, Object Detection, and Semantic Segmentation, which can be evaluated or inferred using cross-validation.
- Optimized for high performance
- Easy to apply cross validation
- Easy to conduct augmentations experiment
- Easy to train the following sota models
- EfficinetNet
- MobileNet v2 or v3
- ResNext
- WideResNet
- EfficientDet
- Shelfnet
UNDER CONSTRUCTION...
A standard deep learning situation.
# Define Data Augmentations
seq = iaa.Sequential([
iaa.Crop(),
iaa.Fliplr(0.5),
iaa.GaussianBlur(sigma=(0, 3.0)),
iaa.Cutout(),
iaa.Multiply()
])
augmentor = blues.augmentors.ClassificationDataAugmentor(seq)
# Define Models
learning_dir = {
'fold1': blues.models.classifications.ResNext(num_classes),
'fold2': blues.models.classifications.WideResNet(num_classes),
'fold3': blues.models.classifications.MobileNetV2(num_classes),
}
training_table = blues.tables.TrainingTable(learning_dir)
# Define a Dataset
dataset = blues.datasets.ClassificationDataset(
dummy_inputs,
dummy_teachers,
batch_size,
blues.resizer.ClassificationResizer((width, height)),
transformers=transformers,
augmentor=augmentor
)
# RUN!!!
trainer = blues.trainers.XTrainer(
training_table,
dataset,
epoch,
result_path,
blues.metrics.accuracy,
callback_functions=callback_functions,
evaluate=True
)
trainer.run()