PyPI 官网下载 | gluoncv-0.5.0b20190903.tar.gz

# Gluon CV Toolkit [](http://ci.mxnet.io/job/gluon-cv/job/master/) [](./LICENSE) [](http://gluon-cv.mxnet.io/coverage.svg) [](https://pypi.python.org/pypi/gluoncv) [](https://pypi.org/project/gluoncv/#history) [](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 # Demo <div align="center"> <img src="docs/_static/short_demo.gif"> </div> <br> Check the HD video at [Youtube](https://www.youtube.com/watch?v=nfpouVAzXt0) or [Bilibili](https://www.bilibili.com/video/av55619231). # 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>| | [Video Action Recognition:](https://gluon-cv.mxnet.io/model_zoo/action_recognition.html) <br/>recognize human actions <br/> in a video. | <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html"><img src="docs/_static/action-recognition.png" alt="action_recognition" height="200"/></a> | <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">TSN</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) - [Video Action Recognition](https://gluon-cv.mxnet.io/build/examples_action_recognition/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.htm