DeepFace

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Keras implementation of the renowned publication "DeepFace: Closing the Gap to Human-Level Performance in Face Verification" by Taigman et al. Pre-trained weights on VGGFace2 dataset.

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최종 버전 다운로드 (.zip)

- - generate_train_labels.py
  - README.md
  - requirements.txt
  - train_on_gpu.py
- __init__.py
- dataset.py
- deepface.py
- index.html
- use_pretrained.md
- train_on_tpu.py
.gitignore
deepface-architecture.png
LICENSE
README.md
requirements.txt
setup.py

// repository documentation

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# DeepFace

## Introduction
Open source implementation of the renowned publication titled ["DeepFace: Closing the Gap to Human-Level Performance in Face Verification"](https://research.fb.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/) by Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf published at [Conference on Computer Vision and Pattern Recognition (CVPR)](http://openaccess.thecvf.com/menu.py) 2014.

![DeepFace (CVPR14) Network Architecture](https://raw.githubusercontent.com/swghosh/DeepFace/master/deepface-architecture.png)

Implementation of this paper have been done using Keras ([tf.keras](https://www.tensorflow.org/guide/keras)). This project and necessary research was supported by the [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc). [GCP](https://cloud.google.com) resources have been used to train a million-scale machine learning model using [Cloud TPUs](https://cloud.google.com/tpu/).

## Requirements

- Python 3.5+
- tensorflow>=1.14.0
- matplotlib

```sh
$ pip install requirements.txt
```

or

```sh
$ python3 -m pip install requirements.txt
```

## Installation

```sh
$ pip install git+https://github.com/swghosh/DeepFace.git
```

DeepFace

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