cnn-facial-landmark

yinguobing/cnn-facial-landmark is ranked 15,607 among these 7 peers; JIA-Lab-research/Seg-Zero is ranked 15,611.

cnn-facial-landmark Facial landmarks detection based on convolution neural network. Here is a sample gif showing the detection result. The model is build with TensorFlow, and training code is provided so you can train your own model with your own datasets. The companion tutorial is also available, which includes background, dataset, preprocessing, model architecture, training and deployment. I tried my best to make them simple and easy to understand for beginners. Feel free to open issues when you are

stuck or have some wonderful ideas to share. Getting Started These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Prerequisites Installing Just git clone this repo and you are good to go. Train & evaluate The following command shows how to train the model for 10 epochs. Training and testing files are required to be stored as TensorFlow Record files. You can generate them by yourself, or checkout

the branch in this repository: face-mesh-generator. Export For PC/Cloud applications TensorFlow's SavedModel is recommended and is the default option. For Mobile/Embedded/IoT devices These devices tend to have constrained resource and TensorFlow Lite is most suitable for this situation. However this is beyond the scope of this project. But don't worry, you will find a more comprehensive project in the next section. Once you have accomplished all the applications above, it's a good time to move

on to a more advanced repo with following features: Support multiple public dataset: WFLW, IBUG, etc. Changelog Update 2021-03-09 A preprocessing layer was added and new model weights provided. Update 2020-06-20 Making the default way of building models. Update 2019-08-08 A new input function is added to export the model to take raw tensor input. Use the argument in the exporting command. This is useful if you want to "freeze" the model later. For

those who are interested in inference with frozen model on image/video/webcam, there is a lightweight module here: check it out. Issue #11 #13 #38 #45 and many others have been resolved. No more in training, and exporting model looks fine now. Update 2019-05-22 Thanks for your patience. I have managed to updated the repo that is used to extract face annotations and generate TFRecord file. Some bugs have been fixed and some minimal sample files have

been added. The training part(this repo) is about to be updated. Update 2019-04-22 This repository now has 199 github stars that is totally beyond my expectation. Whoever you are, wherever you are from and whichever language you speak, I want to say "Thank you! " to you 199 github friends for your interest. Human facial landmark detection is easy to get hands on but also hard enough to demonstrates the power of deep neural networks, that is the

reason I chose for my learning project. Even I had tried my best to keep a exhaustive record that turned