# 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~
温馨提示
人工智能(Artificial Intelligence,简称AI)是一种前沿的计算机科学技术,其核心目标是通过模拟、延伸和拓展人类智能来构建智能机器与系统。它融合了计算机科学、数学、统计学、心理学、神经科学等多个学科的知识,并利用深度学习、机器学习等算法,使计算机能够从数据中学习、理解和推断。 在实际应用中,人工智能体现在诸多领域:如机器人技术,其中机器人不仅能执行预设任务,还能通过感知环境自主决策;语言识别和语音助手技术,如Siri或小爱同学,它们能理解并回应用户的语音指令;图像识别技术,在安防监控、自动驾驶等领域实现对视觉信息的精准分析;自然语言处理技术,应用于搜索引擎、智能客服及社交媒体的情感分析等。 此外,专家系统能够在特定领域提供专业级建议,物联网中的智能设备借助AI优化资源分配与操作效率。人工智能的发展不断改变着我们的生活方式,从工作场景到日常生活,智能化正以前所未有的方式提升生产力、便捷性和生活质量,同时也在挑战伦理边界与社会规则,促使我们重新审视人与技术的关系及其长远影响。
资源推荐
资源详情
资源评论
收起资源包目录
人工智能直通车第二期 - 第八周作业.zip (534个子文件)
BUILD 12KB
BUILD 10KB
BUILD 7KB
BUILD 7KB
BUILD 7KB
BUILD 6KB
BUILD 4KB
BUILD 4KB
BUILD 2KB
BUILD 2KB
BUILD 1KB
BUILD 1KB
BUILD 942B
BUILD 884B
BUILD 718B
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_nas_coco.config 4KB
faster_rcnn_inception_resnet_v2_atrous_oid.config 4KB
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_inception_v2_coco.config 4KB
faster_rcnn_resnet101_pets.config 4KB
faster_rcnn_inception_v2_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_kitti.config 4KB
faster_rcnn_resnet101_voc07.config 3KB
object_detection.iml 455B
slim_walkthrough.ipynb 45KB
object_detection_tutorial.ipynb 9KB
image2.jpg 1.35MB
kites_detections_output.jpg 377KB
dogs_detections_output.jpg 364KB
oid_monkey_3b4168c89cecbc5b.jpg 275KB
oid_bus_72e19c28aac34ed8.jpg 243KB
example_cat.jpg 238KB
image1.jpg 127KB
README.md 24KB
running_pets.md 13KB
oid_inference_and_evaluation.md 11KB
detection_model_zoo.md 7KB
defining_your_own_model.md 7KB
README.md 6KB
configuring_jobs.md 5KB
using_your_own_dataset.md 5KB
running_on_cloud.md 5KB
evaluation_protocols.md 5KB
mobilenet_v1.md 4KB
README.md 3KB
running_locally.md 3KB
preparing_inputs.md 2KB
installation.md 2KB
README.md 1KB
exporting_models.md 786B
CONTRIBUTING.md 765B
running_notebook.md 561B
oid_bbox_trainable_label_map.pbtxt 35KB
mscoco_label_map.pbtxt 5KB
pet_label_map.pbtxt 1KB
pascal_label_map.pbtxt 705B
kitti_label_map.pbtxt 70B
oxford_pet.png 270KB
nongroupof_case_eval.png 259KB
groupof_case_eval.png 237KB
tensorboard2.png 231KB
tf-od-api-logo.png 196KB
mobilenet_v1.png 99KB
tensorboard.png 77KB
preprocessor.proto 15KB
faster_rcnn.proto 6KB
losses.proto 5KB
box_predictor.proto 4KB
ssd.proto 3KB
hyperparams.proto 3KB
optimizer.proto 3KB
train.proto 3KB
ssd_anchor_generator.proto 2KB
input_reader.proto 2KB
eval.proto 2KB
post_processing.proto 1KB
image_resizer.proto 1KB
grid_anchor_generator.proto 1020B
argmax_matcher.proto 979B
string_int_label_map.proto 724B
region_similarity_calculator.proto 672B
box_coder.proto 656B
pipeline.proto 633B
keypoint_box_coder.proto 575B
faster_rcnn_box_coder.proto 531B
anchor_generator.proto 477B
square_box_coder.proto 419B
matcher.proto 418B
共 534 条
- 1
- 2
- 3
- 4
- 5
- 6
资源评论
JJJ69
- 粉丝: 6222
- 资源: 5780
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