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"""
Smoker Detection with LoRA Fine-Tuning
A parameter-efficient approach to binary image classification using
Low-Rank Adaptation (LoRA) on pretrained ResNet34.
"""
from .model import (
LoRALayer,
get_model,
apply_lora_to_model,
count_parameters
)
from .dataset import (
SmokerDataset,
get_transforms,
create_dataloaders
)
from .train import (
train_one_epoch,
validate,
train_model,
get_optimizer_and_criterion
)
from .evaluate import (
evaluate_model,
print_classification_report,
plot_confusion_matrix,
plot_training_history,
get_predictions_with_confidence,
analyze_errors
)
from .utils import (
set_seed,
get_device,
save_checkpoint,
load_checkpoint,
visualize_samples,
print_dataset_info,
create_directories,
count_dataset_images
)
__version__ = '1.0.0'
__author__ = 'Your Name'
__all__ = [
# Model
'LoRALayer',
'get_model',
'apply_lora_to_model',
'count_parameters',
# Dataset
'SmokerDataset',
'get_transforms',
'create_dataloaders',
# Training
'train_one_epoch',
'validate',
'train_model',
'get_optimizer_and_criterion',
# Evaluation
'evaluate_model',
'print_classification_report',
'plot_confusion_matrix',
'plot_training_history',
'get_predictions_with_confidence',
'analyze_errors',
# Utils
'set_seed',
'get_device',
'save_checkpoint',
'load_checkpoint',
'visualize_samples',
'print_dataset_info',
'create_directories',
'count_dataset_images',
] |