deeptuner 0.1.1

A package for fine-tuning deep learning models with Siamese architecture and triplet loss

DeepTuner

Description

DeepTuner is an open source Python package for fine-tuning computer vision (CV) based deep models using Siamese architecture with a triplet loss function. The package supports various model backbones and provides tools for data preprocessing and evaluation metrics.

Installation

To install the package, use the following command:

pip install DeepTuner

Usage

Fine-tuning Models with Siamese Architecture and Triplet Loss

Here is an example of how to use the package for fine-tuning models with Siamese architecture and triplet loss:

import DeepTuner
from DeepTuner import triplet_loss, backbones, data_preprocessing, evaluation_metrics

# Load and preprocess data
data = data_preprocessing.load_data('path/to/dataset')
triplets = data_preprocessing.create_triplets(data)

# Initialize model backbone
model = backbones.get_model('resnet')

# Compile model with triplet loss
model.compile(optimizer='adam', loss=triplet_loss.triplet_loss)

# Train model
model.fit(triplets, epochs=10, batch_size=32)

# Evaluate model
metrics = evaluation_metrics.evaluate_model(model, triplets)
print(metrics)

For more detailed usage and examples, please refer to the documentation.