Updated README

README.md

@@ -1,13 +1,19 @@
 # FaceDetection
-Face Detection in Python using the Viola-Jones algorithm on the CBCL Face Database published by MIT's Center for Biological and Computational Learning. Learn how it works by reading https://medium.com/@anmolparande/understanding-and-implementing-the-viola-jones-image-classification-algorithm-85621f7fe20b
+Face Detection in Python using the Viola-Jones algorithm on the CBCL Face Database published by MIT's Center for Biological and Computational Learning. Learn how it works by reading my tuturial published in The Data Driven Investor on Medium.
 
-# Contents
+Part one (The basic algorithm): https://medium.com/datadriveninvestor/understanding-and-implementing-the-viola-jones-image-classification-algorithm-85621f7fe20b
+
+Part Two (The Attentional Cascade): https://medium.com/datadriveninvestor/understanding-and-implementing-viola-jones-part-two-97ae164ee60f
+
+# Code
 - viola_jones.py
   - An implementation of the Viola-Jones algorithm
-  - Works in Python2 and Python3
-  - Viola, Paul, and Michael Jones. "Rapid object detection using a boosted cascade of simple features." Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. Vol. 1. IEEE, 2001. Works
+  - Viola, Paul, and Michael Jones. "Rapid object detection using a boosted cascade of simple features." Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. Vol. 1. IEEE, 2001.
+- cascade.py
+  - An implementation of the attentional cascade introduced by Paul Viola and Michael Jones
 - face_detection.py
   - Methods to train and test a ViolaJones classifier on the training and test datasets
+  - Methods to train and test a CascadeClassifier on the training and test datasets
 
 # Data
 The data is described at http://cbcl.mit.edu/software-datasets/FaceData2.html, and I downloaded from www.ai.mit.edu/courses/6.899/lectures/faces.tar.gz and compiled into pickle files.
@@ -23,6 +29,14 @@ Test set: 472 faces, 23,573 non-faces
   - An array of tuples. The first element of each tuple is a numpy array representing the image. The second element is its clasification (1 for face, 0 for non-face)
   - 472 faces, 23573 non-face images
 
+# Models
+- 50.pkl
+  - A 50 feature Viola Jones classifier
+- 200.pkl
+  - A 200 feature Viola Jones classifier
+- cascade.pkl
+  - An Attentional Cascade of classifiers looking at 1 feature, 5 features, 10 features, and 50 features.
+
 # Results
 The hyperparameter T for the ViolaJones class represents how many weak classifiers it uses.