deepface + retinaface + chefboost
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@@ -996,6 +996,8 @@ be
 * [OpenFace](https://cmusatyalab.github.io/openface/) - Free and open source face recognition with deep neural networks.
 * [PCV](https://github.com/jesolem/PCV) - Open source Python module for computer vision. **[Deprecated]**
 * [face_recognition](https://github.com/ageitgey/face_recognition) - Face recognition library that recognizes and manipulates faces from Python or from the command line.
+* [deepface](https://github.com/serengil/deepface) - A lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for Python covering cutting-edge models such as VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, Dlib and ArcFace.
+* [retinaface](https://github.com/serengil/retinaface) - deep learning based cutting-edge facial detector for Python coming with facial landmarks
 * [dockerface](https://github.com/natanielruiz/dockerface) - Easy to install and use deep learning Faster R-CNN face detection for images and video in a docker container. **[Deprecated]**
 * [Detectron](https://github.com/facebookresearch/Detectron) - FAIR's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework. **[Deprecated]**
 * [detectron2](https://github.com/facebookresearch/detectron2) - FAIR's next-generation research platform for object detection and segmentation. It is a ground-up rewrite of the previous version, Detectron, and is powered by the PyTorch deep learning framework.
@@ -1085,6 +1087,7 @@ be
 * [auto_ml](https://github.com/ClimbsRocks/auto_ml) - Automated machine learning for production and analytics. Lets you focus on the fun parts of ML, while outputting production-ready code, and detailed analytics of your dataset and results. Includes support for NLP, XGBoost, CatBoost, LightGBM, and soon, deep learning.
 * [machine learning](https://github.com/jeff1evesque/machine-learning) - automated build consisting of a [web-interface](https://github.com/jeff1evesque/machine-learning#web-interface), and set of [programmatic-interface](https://github.com/jeff1evesque/machine-learning#programmatic-interface) API, for support vector machines. Corresponding dataset(s) are stored into a SQL database, then generated model(s) used for prediction(s), are stored into a NoSQL datastore.
 * [XGBoost](https://github.com/dmlc/xgboost) - Python bindings for eXtreme Gradient Boosting (Tree) Library.
+* [ChefBoost](https://github.com/serengil/chefboost) - a lightweight decision tree framework for Python with categorical feature support covering regular decision tree algorithms such as ID3, C4.5, CART, CHAID and regression tree; also some advanved bagging and boosting techniques such as gradient boosting, random forest and adaboost.
 * [Apache SINGA](https://singa.apache.org) - An Apache Incubating project for developing an open source machine learning library.
 * [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Book/iPython notebooks on Probabilistic Programming in Python.
 * [Featureforge](https://github.com/machinalis/featureforge) A set of tools for creating and testing machine learning features, with a scikit-learn compatible API.

I added open source deepface, retinaface and chefboost project here.