# Gluon CV Toolkit
[![Build Status](http://ci.mxnet.io/job/gluon-cv/job/master/badge/icon)](http://ci.mxnet.io/job/gluon-cv/job/master/)
[![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE)
[![Code Coverage](http://gluon-cv.mxnet.io/coverage.svg?)](http://gluon-cv.mxnet.io/coverage.svg)
[![PyPI](https://img.shields.io/pypi/v/gluoncv.svg)](https://pypi.python.org/pypi/gluoncv)
[![PyPI Pre-release](https://img.shields.io/badge/pypi--prerelease-v0.5.0-ff69b4.svg)](https://pypi.org/project/gluoncv/#history)
[![Downloads](http://pepy.tech/badge/gluoncv)](http://pepy.tech/project/gluoncv)
| [Installation](http://gluon-cv.mxnet.io) | [Documentation](http://gluon-cv.mxnet.io) | [Tutorials](http://gluon-cv.mxnet.io) |
GluonCV provides implementations of the state-of-the-art (SOTA) deep learning models in computer vision.
It is designed for engineers, researchers, and
students to fast prototype products and research ideas based on these
models. This toolkit offers four main features:
1. Training scripts to reproduce SOTA results reported in research papers
2. A large number of pre-trained models
3. Carefully designed APIs that greatly reduce the implementation complexity
4. Community supports
# Supported Applications
| Application | Illustration | Available Models |
|:-----------------------:|:---:|:---:|
| [Image Classification:](https://gluon-cv.mxnet.io/model_zoo/classification.html) <br/>recognize an object in an image. | <a href="https://gluon-cv.mxnet.io/model_zoo/classification.html"><img src="docs/_static/image-classification.png" alt="classification" height="200"/></a> | 50+ models, including <br/><a href="https://gluon-cv.mxnet.io/model_zoo/classification.html#resnet">ResNet</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/classification.html#mobilenet">MobileNet</a>, <br/><a href="https://gluon-cv.mxnet.io/model_zoo/classification.html#densenet">DenseNet</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/classification.html#vgg">VGG</a>, ... |
| [Object Detection:](https://gluon-cv.mxnet.io/model_zoo/detection.html) <br/>detect multiple objects with their <br/> bounding boxes in an image. | <a href="https://gluon-cv.mxnet.io/model_zoo/detection.html"><img src="docs/_static/object-detection.png" alt="detection" height="200"/></a> | <a href="https://gluon-cv.mxnet.io/model_zoo/detection.html#faster-rcnn">Faster RCNN</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/detection.html#ssd">SSD</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/detection.html#yolo-v3">Yolo-v3</a> |
| [Semantic Segmentation:](https://gluon-cv.mxnet.io/model_zoo/segmentation.html#semantic-segmentation) <br/>associate each pixel of an image <br/> with a categorical label. | <a href="https://gluon-cv.mxnet.io/model_zoo/segmentation.html#semantic-segmentation"><img src="docs/_static/semantic-segmentation.png" alt="semantic" height="200"/></a> | <a href="https://gluon-cv.mxnet.io/model_zoo/segmentation.html#semantic-segmentation">FCN</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/segmentation.html#semantic-segmentation">PSP</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/segmentation.html#semantic-segmentation">DeepLab v3</a> |
| [Instance Segmentation:](https://gluon-cv.mxnet.io/model_zoo/segmentation.html#instance-segmentation) <br/>detect objects and associate <br/> each pixel inside object area with an <br/> instance label. | <a href="https://gluon-cv.mxnet.io/model_zoo/segmentation.html#instance-segmentation"><img src="docs/_static/instance-segmentation.png" alt="instance" height="200"/></a> | <a href="https://gluon-cv.mxnet.io/model_zoo/segmentation.html#instance-segmentation">Mask RCNN</a>|
| [Pose Estimation:](https://gluon-cv.mxnet.io/model_zoo/pose.html) <br/>detect human pose <br/> from images. | <a href="https://gluon-cv.mxnet.io/model_zoo/pose.html"><img src="docs/_static/pose-estimation.svg" alt="pose" height="200"/></a> | <a href="https://gluon-cv.mxnet.io/model_zoo/pose.html#simple-pose-with-resnet">Simple Pose</a>|
| [GAN:](https://github.com/dmlc/gluon-cv/tree/master/scripts/gan) <br/>generate visually deceptive images | <a href="https://github.com/dmlc/gluon-cv/tree/master/scripts/gan"><img src="https://github.com/dmlc/gluon-cv/raw/master/scripts/gan/wgan/fake_samples_400000.png" alt="lsun" height="200"/></a> | <a href="https://github.com/dmlc/gluon-cv/tree/master/scripts/gan/wgan">WGAN</a>, <a href="https://github.com/dmlc/gluon-cv/tree/master/scripts/gan/cycle_gan">CycleGAN</a> |
| [Person Re-ID:](https://github.com/dmlc/gluon-cv/tree/master/scripts/re-id/baseline) <br/>re-identify pedestrians across scenes | <a href="https://github.com/dmlc/gluon-cv/tree/master/scripts/re-id/baseline"><img src="https://user-images.githubusercontent.com/3307514/46702937-f4311800-cbd9-11e8-8eeb-c945ec5643fb.png" alt="re-id" height="160"/></a> |<a href="https://github.com/dmlc/gluon-cv/tree/master/scripts/re-id/baseline">Market1501 baseline</a> |
# Installation
GluonCV supports Python 2.7/3.5 or later. The easiest way to install is via pip.
## Stable Release
The following commands install the stable version of GluonCV and MXNet:
```bash
pip install gluoncv --upgrade
pip install mxnet-mkl --upgrade
# if cuda 9.2 is installed
pip install mxnet-cu92mkl --upgrade
```
**The latest stable version of GluonCV is 0.4 and depends on mxnet >= 1.4.0**
## Nightly Release
You may get access to latest features and bug fixes with the following commands which install the nightly build of GluonCV and MXNet:
```bash
pip install gluoncv --pre --upgrade
pip install mxnet-mkl --pre --upgrade
# if cuda 9.2 is installed
pip install mxnet-cu92mkl --pre --upgrade
```
There are multiple versions of MXNet pre-built package available. Please refer to [mxnet packages](https://gluon-crash-course.mxnet.io/mxnet_packages.html) if you need more details about MXNet versions.
# Docs ����
GluonCV documentation is available at [our website](https://gluon-cv.mxnet.io/index.html).
# Examples
All tutorials are available at [our website](https://gluon-cv.mxnet.io/index.html)!
- [Image Classification](http://gluon-cv.mxnet.io/build/examples_classification/index.html)
- [Object Detection](http://gluon-cv.mxnet.io/build/examples_detection/index.html)
- [Semantic Segmentation](http://gluon-cv.mxnet.io/build/examples_segmentation/index.html)
- [Instance Segmentation](http://gluon-cv.mxnet.io/build/examples_instance/index.html)
- [Generative Adversarial Network](https://github.com/dmlc/gluon-cv/tree/master/scripts/gan)
- [Person Re-identification](https://github.com/dmlc/gluon-cv/tree/master/scripts/re-id/)
# Resources
Check out how to use GluonCV for your own research or projects.
- For background knowledge of deep learning or CV, please refer to the open source book [*Dive into Deep Learning*](http://diveintodeeplearning.org/). If you are new to Gluon, please check out [our 60-minute crash course](http://gluon-crash-course.mxnet.io/).
- For getting started quickly, refer to notebook runnable examples at [Examples](https://gluon-cv.mxnet.io/build/examples_classification/index.html).
- For advanced examples, check out our [Scripts](http://gluon-cv.mxnet.io/master/scripts/index.html).
- For experienced users, check out our [API Notes](https://gluon-cv.mxnet.io/api/data.datasets.html#).
# Citation
If you feel our code or models helps in your research, please kindly cite our papers:
```
@article{he2018bag,
title={Bag of Tricks for Image Classification with Convolutional Neural Networks},
author={He, Tong and Zhang, Zhi and Zhang, Hang and Zhang, Zhongyue and Xie, Junyuan and Li, Mu},
journal={arXiv preprint arXiv:1812.01187},
year={2018}
}
@article{zhang2019bag,
title={Bag of Freebies for Training Object Detection Neural Networks},
author={Zhang, Zhi and He, Tong and Zhang, Hang and Zhang, Zhongyue and Xie, Junyuan and Li, Mu},
journal={arXiv preprint arXiv:1902.04103},
year={2019}
}```
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
共162个文件
py:138个
json:13个
txt:4个
资源分类:Python库 所属语言:Python 资源全名:gluoncv-0.5.0b20190613.tar.gz 资源来源:官方 安装方法:https://lanzao.blog.csdn.net/article/details/101784059
资源推荐
资源详情
资源评论
收起资源包目录
Python库 | gluoncv-0.5.0b20190613.tar.gz (162个子文件)
setup.cfg 64B
MANIFEST.in 97B
ssd_512_resnet50_v1_voc_int8-symbol.json 345KB
resnet50_v1_int8-symbol.json 254KB
ssd_512_mobilenet1.0_voc_int8-symbol.json 203KB
ssd_512_vgg16_atrous_voc_int8-symbol.json 136KB
ssd_300_vgg16_atrous_voc_int8-symbol.json 124KB
mobilenet1.0_int8-symbol.json 114KB
resnet101_v1d_1.9x.json 10KB
resnet101_v1d_2.2x.json 10KB
resnet50_v1d_1.8x.json 5KB
resnet50_v1d_3.6x.json 5KB
resnet50_v1d_5.9x.json 5KB
resnet50_v1d_8.8x.json 5KB
resnet18_v1b_2.6x.json 2KB
LICENSE 11KB
README.md 8KB
PKG-INFO 1KB
PKG-INFO 1KB
classification.py 57KB
faster_rcnn.py 45KB
resnet.py 44KB
yolo3.py 43KB
resnetv1b.py 41KB
mask_rcnn.py 40KB
nasnet.py 38KB
residual_attentionnet.py 37KB
presets.py 32KB
se_resnet.py 30KB
mobilenet.py 22KB
loss.py 20KB
ssd.py 19KB
rcnn.py 18KB
cifarresnet.py 18KB
resnext.py 17KB
xception.py 17KB
coder.py 16KB
sampler.py 16KB
resnetv1b_pruned.py 14KB
image.py 14KB
dataloader.py 14KB
yolo_target.py 13KB
feature.py 13KB
deeplabv3.py 12KB
batchify.py 12KB
model_store.py 12KB
deeplabv3_plus.py 11KB
inception.py 11KB
densenet.py 11KB
cifarwideresnet.py 11KB
rcnn.py 11KB
pspnet.py 11KB
senet.py 10KB
vgg.py 10KB
model_zoo.py 10KB
voc_detection.py 10KB
cifarresnext.py 10KB
fcn.py 9KB
rpn.py 9KB
simple_pose_resnet.py 9KB
pose.py 9KB
yolo.py 9KB
segbase.py 9KB
coco_detection.py 9KB
bbox.py 9KB
rcnn_target.py 8KB
parallel.py 8KB
detection.py 8KB
segmentation.py 8KB
keypoints.py 8KB
bbox.py 8KB
ssd.py 8KB
instance.py 8KB
squeezenet.py 8KB
coco_instance.py 8KB
vgg_atrous.py 8KB
darknet.py 7KB
rpn_target.py 7KB
detection.py 6KB
segmentation.py 6KB
keypoints.py 5KB
bbox.py 5KB
bbox.py 5KB
segmentation.py 5KB
coco_keypoints.py 5KB
cityscapes.py 5KB
block.py 5KB
mask.py 5KB
lr_scheduler.py 5KB
simple_pose.py 4KB
matcher.py 4KB
export_helper.py 4KB
mask.py 4KB
bbox.py 4KB
heatmap_accuracy.py 4KB
rcnn_target.py 4KB
segmentation.py 4KB
segmentation.py 4KB
image.py 4KB
alexnet.py 4KB
共 162 条
- 1
- 2
资源评论
挣扎的蓝藻
- 粉丝: 13w+
- 资源: 15万+
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