# TensorFlow-Slim image classification model library
[TF-slim](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/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](https://github.com/tensorflow/models/blob/master/research/slim/slim_walkthrough.ipynb),
which provides working examples of how to use TF-Slim for image classification.
For developing or modifying your own models, see also the [main TF-Slim page](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim).
## Contacts
Maintainers of TF-slim:
* Nathan Silberman,
github: [nathansilberman](https://github.com/nathansilberman)
* Sergio Guadarrama, github: [sguada](https://github.com/sguada)
## Table of contents
<a href="#Install">Installation and setup</a><br>
<a href='#Data'>Preparing the datasets</a><br>
<a href='#Pretrained'>Using pre-trained models</a><br>
<a href='#Training'>Training from scratch</a><br>
<a href='#Tuning'>Fine tuning to a new task</a><br>
<a href='#Eval'>Evaluating performance</a><br>
<a href='#Export'>Exporting Inference Graph</a><br>
<a href='#Troubleshooting'>Troubleshooting</a><br>
# Installation
<a id='Install'></a>
In this section, we describe the steps required to install the appropriate
prerequisite packages.
## Installing latest version of TF-slim
TF-Slim is available as `tf.contrib.slim` via TensorFlow 1.0. To test that your
installation is working, execute the following command; it should run without
raising any errors.
```
python -c "import tensorflow.contrib.slim as slim; eval = slim.evaluation.evaluate_once"
```
## Installing the TF-slim image models library
To use TF-Slim for image classification, you also have to install
the [TF-Slim image models library](https://github.com/tensorflow/models/tree/master/research/slim),
which is not part of the core TF library.
To do this, check out the
[tensorflow/models](https://github.com/tensorflow/models/) repository as follows:
```bash
cd $HOME/workspace
git clone https://github.com/tensorflow/models/
```
This will put the TF-Slim image models library in `$HOME/workspace/models/research/slim`.
(It will also create a directory called
[models/inception](https://github.com/tensorflow/models/tree/master/research/inception),
which contains an older version of slim; you can safely ignore this.)
To verify that this has worked, execute the following commands; it should run
without raising any errors.
```
cd $HOME/workspace/models/research/slim
python -c "from nets import cifarnet; mynet = cifarnet.cifarnet"
```
# Preparing the datasets
<a id='Data'></a>
As part of this library, we've included scripts to download several popular
image datasets (listed below) and convert them to slim format.
Dataset | Training Set Size | Testing Set Size | Number of Classes | Comments
:------:|:---------------:|:---------------------:|:-----------:|:-----------:
Flowers|2500 | 2500 | 5 | Various sizes (source: Flickr)
[Cifar10](https://www.cs.toronto.edu/~kriz/cifar.html) | 60k| 10k | 10 |32x32 color
[MNIST](http://yann.lecun.com/exdb/mnist/)| 60k | 10k | 10 | 28x28 gray
[ImageNet](http://www.image-net.org/challenges/LSVRC/2012/)|1.2M| 50k | 1000 | Various sizes
## Downloading and converting to TFRecord format
For each dataset, we'll need to download the raw data and convert it to
TensorFlow's native
[TFRecord](https://www.tensorflow.org/versions/r0.10/api_docs/python/python_io.html#tfrecords-format-details)
format. Each TFRecord contains a
[TF-Example](https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/core/example/example.proto)
protocol buffer. Below we demonstrate how to do this for the Flowers dataset.
```shell
$ DATA_DIR=/tmp/data/flowers
$ python download_and_convert_data.py \
--dataset_name=flowers \
--dataset_dir="${DATA_DIR}"
```
When the script finishes you will find several TFRecord files created:
```shell
$ ls ${DATA_DIR}
flowers_train-00000-of-00005.tfrecord
...
flowers_train-00004-of-00005.tfrecord
flowers_validation-00000-of-00005.tfrecord
...
flowers_validation-00004-of-00005.tfrecord
labels.txt
```
These represent the training and validation data, sharded over 5 files each.
You will also find the `$DATA_DIR/labels.txt` file which contains the mapping
from integer labels to class names.
You can use the same script to create the mnist and cifar10 datasets.
However, for ImageNet, you have to follow the instructions
[here](https://github.com/tensorflow/models/blob/master/research/inception/README.md#getting-started).
Note that you first have to sign up for an account at image-net.org.
Also, the download can take several hours, and could use up to 500GB.
## Creating a TF-Slim Dataset Descriptor.
Once the TFRecord files have been created, you can easily define a Slim
[Dataset](https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/contrib/slim/python/slim/data/dataset.py),
which stores pointers to the data file, as well as various other pieces of
metadata, such as the class labels, the train/test split, and how to parse the
TFExample protos. We have included the TF-Slim Dataset descriptors
for
[Cifar10](https://github.com/tensorflow/models/blob/master/research/slim/datasets/cifar10.py),
[ImageNet](https://github.com/tensorflow/models/blob/master/research/slim/datasets/imagenet.py),
[Flowers](https://github.com/tensorflow/models/blob/master/research/slim/datasets/flowers.py),
and
[MNIST](https://github.com/tensorflow/models/blob/master/research/slim/datasets/mnist.py).
An example of how to load data using a TF-Slim dataset descriptor using a
TF-Slim
[DatasetDataProvider](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/data/dataset_data_provider.py)
is found below:
```python
import tensorflow as tf
from datasets import flowers
slim = tf.contrib.slim
# Selects the 'validation' dataset.
dataset = flowers.get_split('validation', DATA_DIR)
# Creates a TF-Slim DataProvider which reads the dataset in the background
# during both training and testing.
provider = slim.dataset_data_provider.DatasetDataProvider(dataset)
[image, label] = provider.get(['image', 'label'])
```
## An automated script for processing ImageNet data.
Training a model with the ImageNet dataset is a common request. To facilitate
working with the ImageNet dataset, we provide an automated script for
downloading and processing the ImageNet dataset into the native TFRecord
format.
The TFRecord format consists of a set of sharded files where each entry is a serialized `tf.Example` proto. Each `tf.Example` proto contains the ImageNet image (JPEG encoded) as well as metadata such as label and bounding box information.
We provide a single [script](datasets/download_and_preprocess_imagenet.sh) for
downloading and converting ImageNet data to TFRecord format. Downloading and
preprocessing the data may take several hours (up to half a day) depending on
your network and computer speed. Please be patient.
To begin, you will need to sign up for an account with [ImageNet]
(http://image-net.org) to gain access to the data. Look for the sign up page,
create an account and request an access key to download the data.
After you have `USERNAME` and `PASSWORD`, you are ready to run our script. Make
sure that your hard disk has at least 500 GB of free space for downloading and
storing
没有合适的资源?快使用搜索试试~ 我知道了~
基于TensorFlow的项目实例
共6982个文件
jpg:6113个
py:515个
png:104个
1星 需积分: 45 59 下载量 121 浏览量
2018-05-29
23:28:39
上传
评论 6
收藏 152.7MB ZIP 举报
温馨提示
实例包括: CNN、图像相关:包含图像分类、目标检测、人脸识别、风格迁移,同时包含GAN、cGAN、CycleGAN等和GAN相关的内容 RNN、序列相关:文本生成、序列分类、训练词嵌入、时间序列预测、机器翻译等等。 强化学习:主要复现一些基础的算法,如Q Learning、SARSA、Deep Q Learning等。
资源推荐
资源详情
资源评论
收起资源包目录
基于TensorFlow的项目实例 (6982个子文件)
BUILD 10KB
BUILD 9KB
BUILD 8KB
BUILD 7KB
BUILD 6KB
BUILD 5KB
BUILD 5KB
BUILD 4KB
BUILD 3KB
BUILD 2KB
BUILD 2KB
BUILD 2KB
BUILD 1KB
BUILD 942B
BUILD 649B
BUILD 595B
BUILD 585B
ssd_mobilenet_v1_coco.config 5KB
ssd_mobilenet_v1_pets.config 5KB
ssd_inception_v2_coco.config 5KB
ssd_inception_v2_pets.config 5KB
faster_rcnn_inception_resnet_v2_atrous_coco.config 4KB
faster_rcnn_inception_resnet_v2_atrous_pets.config 4KB
faster_rcnn_resnet152_coco.config 4KB
faster_rcnn_resnet101_coco.config 4KB
faster_rcnn_resnet50_coco.config 4KB
faster_rcnn_resnet101_pets.config 4KB
faster_rcnn_resnet152_pets.config 4KB
faster_rcnn_resnet50_pets.config 4KB
rfcn_resnet101_coco.config 4KB
rfcn_resnet101_pets.config 4KB
faster_rcnn_resnet101_voc07.config 3KB
font-awesome.min.css 21KB
main.css 11KB
fakeLoader.css 8KB
period_trend.csv 8KB
multivariate_periods.csv 7KB
deen_output 324KB
deen_ref_bpe 372KB
Dockerfile 4KB
Dockerfile 4KB
Dockerfile 853B
Dockerfile 853B
fontawesome-webfont.eot 55KB
terraform.tfvars.example 105B
terraform.tfvars.example 105B
training.gif 14.59MB
single3.gif 320KB
single2.gif 319KB
single4.gif 319KB
single1.gif 318KB
.gitignore 2KB
.gitignore 1KB
.gitignore 1KB
.gitignore 1KB
.gitignore 1KB
.gitignore 1KB
.gitignore 1KB
.gitignore 1KB
.gitignore 989B
.gitignore 876B
.gitignore 100B
.gitignore 89B
.gitignore 89B
.gitignore 50B
index.html 28KB
index.html 21KB
index.html 21KB
slim_walkthrough.ipynb 45KB
slim_walkthrough.ipynb 45KB
object_detection_tutorial.ipynb 9KB
test.ipynb 2KB
Untitled.ipynb 72B
image2.jpg 1.35MB
facades-sheet.jpg 1.27MB
facades-sheet.jpg 1.27MB
edges2handbags-sheet.jpg 933KB
edges2handbags-sheet.jpg 933KB
edges2cats-sheet.jpg 807KB
edges2cats-sheet.jpg 807KB
denoised_starry.jpg 715KB
edges2shoes-sheet.jpg 677KB
edges2shoes-sheet.jpg 677KB
examples.jpg 469KB
examples.jpg 469KB
udnie.jpg 454KB
example_captions.jpg 421KB
kites_detections_output.jpg 377KB
candy.jpg 367KB
dogs_detections_output.jpg 364KB
feathers.jpg 314KB
starry.jpg 301KB
mosaic.jpg 285KB
denoised_starry.jpg 253KB
test3.jpg 252KB
example_cat.jpg 238KB
wave.jpg 229KB
feathers.jpg 227KB
test5.jpg 215KB
test.jpg 215KB
共 6982 条
- 1
- 2
- 3
- 4
- 5
- 6
- 70
资源评论
- mengweilil2018-06-01只有代码,没有书。代码是github上可以下载的。
flyingzerozero
- 粉丝: 2
- 资源: 8
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功