# Face-similarity
Face-similarity CNN using Tensorflow Eager execution.
Medium article: [How to train your own FaceID ConvNet using TensorFlow Eager execution](https://medium.freecodecamp.org/how-to-train-your-own-faceid-cnn-using-tensorflow-eager-execution-6905afe4fd5a)
This implementation uses DenseNets with contrastive loss.
Reference Papers:
- [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993)
- [Dimensionality Reduction by Learning an Invariant Mapping](https://ieeexplore.ieee.org/document/1640964)
## Dependencies:
- Python 3.6.x
- Tensorflow 1.10.1
## Inference
### Google Colaboratory
- Just open the `inference.ipynb` and select the option to open on Colab.
### Running locally
1- Download the pre-trained model using the following link.
* Place the `tboard_logs` folder in the root folder of the project.
- [Tensorflow pre-trained model](https://www.dropbox.com/sh/qgz0gw6pqkn64gq/AAAi4eQ97f2yNo8wRQ4FEx-3a?dl=0)
2- Download the following test dataset (TfRecords format).
* Place the `dataset` folder in the root folder of the project.
- [Download test dataset](https://www.dropbox.com/sh/qgz0gw6pqkn64gq/AAAi4eQ97f2yNo8wRQ4FEx-3a?dl=0)
3- Run the jupyter notebook `inference.ipynb`
* Run the notebook. Adjust the dataset paths accordingly.
## Results
# facialAuthModel