| #!/usr/bin/python3 | |
| __base__ = 'https://github.com/thomdixon/pysha2/blob/master/sha2/sha256.py' | |
| __author__ = 'Lukas Prokop' | |
| __license__ = 'MIT' | |
| import copy | |
| import struct | |
| import binascii |
This is a model from the paper:
A Deep Siamese Network for Scene Detection in Broadcast Videos
Lorenzo Baraldi, Costantino Grana, Rita Cucchiara
Proceedings of the 23rd ACM International Conference on Multimedia, 2015
Please cite the paper if you use the models.
| #! /bin/bash | |
| # Upgrade | |
| sudo aptitude update | |
| sudo aptitude full-upgrade -y | |
| # Install CUDA | |
| wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_6.5-14_amd64.deb | |
| sudo dpkg -i cuda-repo-ubuntu1404_6.5-14_amd64.deb | |
| sudo aptitude update |
##VGG19 model for Keras
This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ |
| # Alec Radford, Indico, Kyle Kastner | |
| # License: MIT | |
| """ | |
| Convolutional VAE in a single file. | |
| Bringing in code from IndicoDataSolutions and Alec Radford (NewMu) | |
| Additionally converted to use default conv2d interface instead of explicit cuDNN | |
| """ | |
| import theano | |
| import theano.tensor as T | |
| from theano.compat.python2x import OrderedDict |