versions/master/api/python/docs/_sources/tutorials/packages/gluon/image/index.rst

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Image Tutorials
===============

These tutorials will help you learn how to create and use models that work with
images and other computer vision tasks.
Most of these tutorials use the `MXNet GluonCV toolkit <https://gluon-cv.mxnet.io/>`__.

Basic Image Tutorials
---------------------

.. container:: cards

   .. card::
      :title: MNIST
      :link: mnist.html

      How to create a convolutional neural network for handwritten digit recognition.


GluonCV Toolkit Tutorials
-------------------------

These tutorials link to the MXNet GluonCV Toolkit website.

.. container:: cards

   .. card::
      :title: Prepare Datasets
      :link: https://gluon-cv.mxnet.io/build/examples_datasets/index.html

      How to use built-in MXNet GluonCV features for loading and preparing both common & custom datasets.

   .. card::
      :title: Image Classification
      :link: https://gluon-cv.mxnet.io/build/examples_classification/index.html

      Pretrained models for inference, fine-tune models, train your own model
      on ImageNet, and more.

   .. card::
      :title: Object Detection
      :link: https://gluon-cv.mxnet.io/build/examples_detection/index.html

      Learn how to use Single shot detector (SSD), RCNN, and YOLO models.

   .. card::
      :title: Semantic Segmentation
      :link: https://gluon-cv.mxnet.io/build/examples_segmentation/index.html

      Learn how to use and train models that can identify and segment objects in an image.

   .. card::
      :title: Instance Segmentation
      :link: https://gluon-cv.mxnet.io/build/examples_instance/index.html

      Learn how to use and train models the perform a variation of semantic
      segmentation that also classifies similar objects into discrete entities.

   .. card::
      :title: Pose Estimation
      :link: https://gluon-cv.mxnet.io/build/examples_pose/index.html

      Learn how to use a simple Pose network that predicts the heatmap for each
      joint then map it to the coordinates on the original image.


.. toctree::
   :hidden:
   :maxdepth: 1
   :glob:

   *