# Gluon CV Toolkit
[![Build Status](http://ci.mxnet.io/buildStatus/icon?job=gluon-cv%2Fmaster)](http://ci.mxnet.io/job/gluon-cv/job/master/)
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[![Downloads](http://pepy.tech/badge/gluoncv)](http://pepy.tech/project/gluoncv)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/resnest-split-attention-networks/semantic-segmentation-on-ade20k)](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k?p=resnest-split-attention-networks)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/resnest-split-attention-networks/object-detection-on-coco)](https://paperswithcode.com/sota/object-detection-on-coco?p=resnest-split-attention-networks)
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[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/resnest-split-attention-networks/panoptic-segmentation-on-coco-panoptic)](https://paperswithcode.com/sota/panoptic-segmentation-on-coco-panoptic?p=resnest-split-attention-networks)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/resnest-split-attention-networks/image-classification-on-imagenet)](https://paperswithcode.com/sota/image-classification-on-imagenet?p=resnest-split-attention-networks)
| [Installation](https://gluon-cv.mxnet.io/install.html) | [Documentation](https://gluon-cv.mxnet.io) | [Tutorials](https://gluon-cv.mxnet.io/tutorials/index.html) |
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. Supports both PyTorch and MXNet
3. A large number of pre-trained models
4. Carefully designed APIs that greatly reduce the implementation complexity
5. 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">ICNet</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/segmentation.html#semantic-segmentation">DeepLab-v3</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/segmentation.html#semantic-segmentation">DeepLab-v3+</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/segmentation.html#semantic-segmentation">DANet</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/segmentation.html#semantic-segmentation">FastSCNN</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> | MXNet: <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">TSN</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">C3D</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">I3D</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">P3D</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">R3D</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">R2+1D</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">Non-local</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">SlowFast</a> <br/> PyTorch: <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">TSN</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">I3D</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">I3D_slow</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">R2+1D</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">Non-local</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">CSN</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/SlowFast.html">TSN</a>, <a href="https://gluon-cv.mxnet.io/model_zoo/action_recognition.html">TPN</a> |
| [Depth Prediction:](https://gluon-cv.mxnet.io/model_zoo/depth.html) <br/>predict depth map <br/> from images. | <a href="https://gluon-cv.mxnet.io/model_zoo/depth.html"><img src="docs/_static/depth.png" alt="depth" height="200"/></a> | <a href="https://gluon-cv.mxnet.io/model_zoo/depth.html#kitti-dataset">Monodepth2</a>|
| [GAN:](https://github.com/dmlc/gluon-cv/tree/master/scripts/gan) <br/>generate visually deceptive images | <a href="https://github.
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