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TensorFlow-Slim 图像分类库

允许用户在不编码的情况下,在图形用户界面中进行预训练神经网络的迁移学习。

TF-slim是用于定义,训练和评估复杂模型的TensorFlow(tensorflow.contrib.slim)的新型轻量级高级API。
该目录包含用于训练和评估使用TF-slim的几种广泛使用的卷积神经网络(CNN)图像分类模型的代码。
它包含脚本,允许您从头开始训练模型或从预训练的网络权重微调它们。
它还包含用于下载标准图像数据集的代码,将其转换为TensorFlow 的原生
TFRecord 格式,并使用 TF-Slim 的数据读取和序列实用程序进行读取。
您可以轻松地对任何这些数据集上的任何模型进行训练,如下所示。
我们还包括一个jupyter
notebook,它提供了如何使用TF-Slim进行图像分类的工作示例。

It allows user to do transfer learning of pre-trained neural network in
GUI without coding.

TF-slim is a new lightweight high-level API of TensorFlow
(tensorflow.contrib.slim) for defining, training and evaluating complex
models. This directory contains code for training and evaluating several
widely used Convolutional Neural Network (CNN) image classification
models using TF-slim. It contains scripts that will allow you to train
models from scratch or fine-tune them from pre-trained network weights.
It also contains code for downloading standard image datasets,
converting them to TensorFlow’s native TFRecord format and reading them
in using TF-Slim’s data reading and queueing utilities. You can easily
train any model on any of these datasets, as we demonstrate below. We’ve
also included a jupyter notebook, which provides working examples of how
to use TF-Slim for image classification.

用户能够在该GUI中分析预训练网络。

项目地址:
tensorflownews
http://www.tensorflownews.com

User can analyze the pre-trained network in this app.

还允许用户修改图像增强器信息和训练选项。

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