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+# ProtoPNet
+
+This code package implements the prototypical part network (ProtoPNet)
+from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition"
+(to appear at NeurIPS 2019), by Chaofan Chen* (Duke University), Oscar Li* (Duke University),
+Chaofan Tao (Duke University), Alina Jade Barnett (Duke University),
+Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University)
+(* denotes equal contribution).
+
+This code package was SOLELY developed by the authors at Duke University,
+and licensed under MIT License (see LICENSE for more information regarding the use
+and the distribution of this code package).
+
+Prerequisites: PyTorch, NumPy, cv2, Augmentor (https://github.com/mdbloice/Augmentor)
+Recommended hardware: 4 NVIDIA Tesla P-100 GPUs or 8 NVIDIA Tesla K-80 GPUs
+
+Instructions for preparing the data:
+1. Download the dataset CUB_200_2011.tgz from http://www.vision.caltech.edu/visipedia/CUB-200-2011.html
+2. Unpack CUB_200_2011.tgz
+3. Crop the images using information from bounding_boxes.txt (included in the dataset)
+4. Split the cropped images into training and test sets, using train_test_split.txt (included in the dataset)
+5. Put the cropped training images in the directory "./datasets/cub200_cropped/train_cropped/"
+6. Put the cropped test images in the directory "./datasets/cub200_cropped/test_cropped/"
+7. Augment the training set using img_aug.py (included in this code package)
+   -- this will create an augmented training set in the following directory:
+      "./datasets/cub200_cropped/train_cropped_augmented/"
+
+Instructions for training the model:
+1. In settings.py, provide the appropriate strings for data_path, train_dir, test_dir,
+train_push_dir:
+(1) data_path is where the dataset resides
+    -- if you followed the instructions for preparing the data, data_path should be "./datasets/cub200_cropped/"
+(2) train_dir is the directory containing the augmented training set
+    -- if you followed the instructions for preparing the data, train_dir should be data_path + "train_cropped_augmented/"
+(3) test_dir is the directory containing the test set
+    -- if you followed the instructions for preparing the data, test_dir should be data_path + "test_cropped/"
+(4) train_push_dir is the directory containing the original (unaugmented) training set
+    -- if you followed the instructions for preparing the data, train_push_dir should be data_path + "train_cropped/"
+2. Run main.py
+
+Instructions for finding the nearest prototypes to a test image:
+1. Run local_analysis.py and supply the following arguments:
+-gpuid is the GPU device ID(s) you want to use (optional, default '0')
+-modeldir is the directory containing the model you want to analyze
+-model is the filename of the saved model you want to analyze
+-imgdir is the directory containing the image you want to analyze
+-img is the filename of the image you want to analyze
+-imgclass is the (0-based) index of the correct class of the image
+
+Instructions for finding the nearest patches to each prototype:
+1. Run global_analysis.py and supply the following arguments:
+-gpuid is the GPU device ID(s) you want to use (optional, default '0')
+-modeldir is the directory containing the model you want to analyze
+-model is the filename of the saved model you want to analyze
+
+Instructions for pruning the prototypes from a saved model:
+1. Run run_pruning.py and supply the following arguments:
+-gpuid is the GPU device ID(s) you want to use (optional, default '0')
+-modeldir is the directory containing the model you want to prune prototypes from
+-model is the filename of the saved model you want to prune prototypes from
+Note: the prototypes in the model must already have been projected (pushed) onto
+the nearest latent training patches, before running this script
+
+Instructions for combining several ProtoPNet models (Jupyter Notebook required):
+1. Run the Jupyter Notebook combine_models.ipynb