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TF-Slim: A high level library to define complex models in TensorFlow
martes, 30 de agosto de 2016
Posted by Nathan Silberman and Sergio Guadarrama, Google Research
Earlier this year,
we released
a TensorFlow implementation of a state-of-the-art image classification model known as
Inception-V3
. This code allowed users to train the model on the
ImageNet classification dataset
via synchronized gradient descent, using either a single local machine or a cluster of machines. The Inception-V3 model was built on an experimental
TensorFlow
library called
TF-Slim
, a lightweight package for defining, training and evaluating models in TensorFlow. The TF-Slim library provides common abstractions which enable users to define models quickly and concisely, while keeping the model architecture transparent and its hyperparameters explicit.
Since that release, TF-Slim has grown substantially, with many types of
layers
,
loss functions
, and
evaluation metrics
added, along with handy routines for
training
and
evaluating
models. These routines take care of all the details you need to worry about when working at scale, such as reading data in parallel, deploying models on multiple machines, and more. Additionally, we have created the
TF-Slim Image Models library
, which provides definitions and training scripts for many widely used image classification models, using standard datasets. TF-Slim and its components are already widely used within Google, and many of these improvements have already been integrated into
tf.contrib.slim
.
Today, we are proud to share the latest release of TF-Slim with the TF community. Some highlights of this release include:
Many new kinds of
layers
(such as
Atrous Convolution
and
Deconvolution
) enabling a much richer family of neural network architectures.
Support for more loss functions and
evaluation metrics
(e.g., mAP, IoU).
A
deployment library
to make it easier to perform synchronous or asynchronous training using multiple GPUs/CPUs, on the same machine or on multiple machines.
Code
to define and train many widely used image classification models (e.g.,
Inception
[1][2][3]
,
VGG
[4]
,
AlexNet
[5]
,
ResNet
[6]
).
Pre-trained
model weights for the above image classification models. These models have been trained on the
ImageNet classification dataset
, but can be used for many other computer vision tasks. As a simple example, we provide code to
fine-tune
these classifiers to a new set of output labels.
Tools
to easily process standard image datasets, such as
ImageNet
,
CIFAR10
and
MNIST
.
Want to get started using TF-Slim? See the
README
for details. Interested in working with image classification models? See these
instructions
or this
Jupyter notebook
.
The release of the TF-Slim library and the pre-trained model zoo has been the result of widespread collaboration within Google Research. In particular we want to highlight the vital contributions of the following researchers:
TF-Slim:
Sergio Guadarrama, Nathan Silberman.
Model Definitions and Checkpoints:
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Jon Shlens, Zbigniew Wojna, Vivek Rathod, George Papandreou, Alex Alemi
Systems Infrastructure:
Jon Shlens, Matthieu Devin, Martin Wicke
Jupyter notebook:
Nathan Silberman, Kevin Murphy
References:
[1]
Going deeper with convolutions
,
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, CVPR 2015
[2]
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe, Christian Szegedy, ICML 2015
[3]
Rethinking the Inception Architecture for Computer Vision
,
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna, arXiv technical report 2015
[4]
Very Deep Convolutional Networks for Large-Scale Image Recognition
,
Karen Simonyan, Andrew Zisserman, ICLR 2015
[5]
ImageNet Classification with Deep Convolutional Neural Networks
,
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, NIPS 2012
[6]
Deep Residual Learning for Image Recognition
,
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, CVPR 2016
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