https://github.com/larq/zoo
Reference implementations of popular Binarized Neural Networks
https://github.com/larq/zoo
binarized-neural-networks deep-learning keras larq machine-learning neural-networks pretrained-models python reproducible-research tensorflow
Last synced: about 1 month ago
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Reference implementations of popular Binarized Neural Networks
- Host: GitHub
- URL: https://github.com/larq/zoo
- Owner: larq
- License: apache-2.0
- Created: 2019-05-02T17:02:08.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2024-03-13T23:22:39.000Z (about 1 year ago)
- Last Synced: 2024-03-14T22:22:58.467Z (about 1 year ago)
- Topics: binarized-neural-networks, deep-learning, keras, larq, machine-learning, neural-networks, pretrained-models, python, reproducible-research, tensorflow
- Language: Python
- Homepage: https://docs.larq.dev/zoo
- Size: 1.23 MB
- Stars: 102
- Watchers: 14
- Forks: 17
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# Larq Zoo
[](https://github.com/larq/zoo/actions?workflow=Unittest) [](https://codecov.io/github/larq/zoo?branch=main) [](https://pypi.org/project/larq-zoo/) [](https://pypi.org/project/larq-zoo/) [](https://github.com/plumerai/larq-zoo/blob/main/LICENSE) [](https://github.com/ambv/black)
For more information, see [larq.dev/zoo](https://docs.larq.dev/zoo/).
*Larq Zoo is part of a family of libraries for BNN development; you can also check out [Larq](https://github.com/larq/larq) for building and training BNNs and [Larq Compute Engine](https://github.com/larq/compute-engine) for deployment on mobile and edge devices.*
## Requirements
Before installing Larq Zoo, please install:
- [Python](https://python.org) version `3.9` or `3.10`
- [Tensorflow](https://www.tensorflow.org/install) version `2.4` up to `2.12`.## Installation
You can install Larq Zoo with Python's [pip](https://pip.pypa.io/en/stable/) package manager:
```shell
pip install larq-zoo
```## About
Larq Zoo is being developed by a team of deep learning researchers and engineers at Plumerai to help accelerate both our own research and the general adoption of Binarized Neural Networks.