From e48b7d060f522b275424442fc13d890a428db700 Mon Sep 17 00:00:00 2001 From: Andrew Gibiansky Date: Fri, 4 Jan 2013 16:36:56 -0500 Subject: [PATCH] fixed formatting --- dogs/README.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/dogs/README.md b/dogs/README.md index 6375da7..140e5cd 100644 --- a/dogs/README.md +++ b/dogs/README.md @@ -1,4 +1,4 @@ -== Convolutional Neural Networks for Dog Breed Identification == +__Convolutional Neural Networks for Dog Breed Identification__ Using convolutional neural networks, this project aims to be able to distinguish what breed a dog is given its photograph. Using five different breeds with a total of approximately 25,000 training images, the neural network classifier achieves a classification rate of approximately 90%. @@ -13,13 +13,13 @@ as well as Boxers. This yields a vector of "probabilities" for each image - sinc each image is converted into a string of 10\*2 = 20 numbers between zero and one. A small fully connected neural network is then trained on this data in order to give a final multi-class prediction. The classification rate on an independent test set is approximately 90%. -Files: - - neuralnetworks.py - implementation of neural networks and data processing using Theano - - mnist/ - example use of neuralnetworks.py library to recognize MNIST digits (reaches 98% recognition rate) - - all\_dogs.py - train a multi-class classifier for different breeds - - dogs.py - trains pairwise comparison networks - - pairs.py - run pairwise comparison networks on all input data - - pairtrain.py - train a post-processing step to convert the output of the pairwise networks into a multi-class prediction +__Files:__ +* neuralnetworks.py - implementation of neural networks and data processing using Theano +* mnist/ - example use of neuralnetworks.py library to recognize MNIST digits (reaches 98% recognition rate) +* all\_dogs.py - train a multi-class classifier for different breeds +* dogs.py - trains pairwise comparison networks +* pairs.py - run pairwise comparison networks on all input data +* pairtrain.py - train a post-processing step to convert the output of the pairwise networks into a multi-class prediction -Data: - - imgs.tar.gz - dog photos sorted by breed. Each photo was resized to a 64x64 3-color image (losing aspect ratio). Data were obtained using Petfinder.com API. +__Data:__ +* imgs.tar.gz - dog photos sorted by breed. Each photo was resized to a 64x64 3-color image (losing aspect ratio). Data were obtained using Petfinder.com API.