Skip to main content

Deep Learning Tools for Pytorch

Project description

Deep Learning Tools for Pytorch

Python >= 3.8

A package that contains tools for deep learning model. We add the registry class which can make developer use the registry method to manage their models or functions conveniently.

In other side, we will provide our work in classification task for developer, which can use the model directly.

Installation

pip install dlts

Example for using

from typing import Callable

from dlts import Registry

# Example usage
registry = Registry(registry_name="example_registry", base_type=Callable)

@registry.register("example_function")
def example_function(x: int) -> int:
    return x * 2

print(registry.get("example_function")(5))  # Output: 10
print(registry.keys())  # Output: dict_keys(['example_function'])

Papers

BibTex

@article{sheng2024lightweight,
  title={A lightweight hybrid model with location-preserving ViT for efficient food recognition},
  author={Sheng, Guorui and Min, Weiqing and Zhu, Xiangyi and Xu, Liang and Sun, Qingshuo and Yang, Yancun and Wang, Lili and Jiang, Shuqiang},
  journal={Nutrients},
  volume={16},
  number={2},
  pages={200},
  year={2024},
  publisher={MDPI}
}

Update

  • 0.0.2 - We add the EHFR-Net model in the tools.
  • 0.0.1 - It is an official version.
  • 0.0.1alpha2 - It is a test version.

Future Plans

  • Add some models which are used in the food classification.
  • Add more tools for deep learning model management.

License

mDeep Learning Tools for Pytorch is MIT licensed. See the LICENSE for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dlts-0.0.2.post1.tar.gz (11.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

dlts-0.0.2.post1-py3-none-any.whl (15.7 kB view details)

Uploaded Python 3

File details

Details for the file dlts-0.0.2.post1.tar.gz.

File metadata

  • Download URL: dlts-0.0.2.post1.tar.gz
  • Upload date:
  • Size: 11.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.8.20

File hashes

Hashes for dlts-0.0.2.post1.tar.gz
Algorithm Hash digest
SHA256 b65dc1df2fdf9df25dbe385e27ea5cd78ea4fb584a26c9f08018c37ba790e6fa
MD5 9221582245c29032eda3b633f1345a6a
BLAKE2b-256 18024756525640c117357b62373836f7f8c972fcc3db198c655c47beac672235

See more details on using hashes here.

File details

Details for the file dlts-0.0.2.post1-py3-none-any.whl.

File metadata

  • Download URL: dlts-0.0.2.post1-py3-none-any.whl
  • Upload date:
  • Size: 15.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.8.20

File hashes

Hashes for dlts-0.0.2.post1-py3-none-any.whl
Algorithm Hash digest
SHA256 4ca9609a88920a5c41190857bf767df5fc5a84ea0f5d3cba9d6d2384f44baca6
MD5 6ef703ae5c99e40226a96a6dde4955d6
BLAKE2b-256 687c44fb713a4c297a42cebd1f878bbe7ca0c181a942eb4c70dff70ee463a666

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page