Create an overview page for the TF vision tutorials, with added focus on KerasCV.

PiperOrigin-RevId: 555636399

Author: pcoet

site/en/tutorials/_toc.yaml

@@ -100,6 +100,11 @@ toc:
 - title: "Vision"
   style: accordion
   section:
+  - title: "Computer vision"
+    path: /tutorials/images
+  - title: "KerasCV"
+    path: https://keras.io/keras_cv/
+    status: external
   - title: "Convolutional Neural Network"
     path: /tutorials/images/cnn
   - title: "Image classification"

site/en/tutorials/images/index.md

@@ -0,0 +1,113 @@
+# Computer vision with TensorFlow
+
+TensorFlow provides a number of computer vision (CV) and image classification
+tools. This document introduces some of these tools and provides an overview of
+resources to help you get started with common CV tasks.
+
+## Vision libraries and tools
+
+TensorFlow provides CV tools through the higher-level Keras libraries and the
+lower-level `tf.image` module. For most use cases, the Keras libraries
+will be more convenient than the built-in TensorFlow alternatives.
+But if the Keras options don't fit your use case, or you want lower-level
+control over image preprocessing, you might need the lower-level TensorFlow
+tools.
+
+### KerasCV
+
+If you're just getting started with a CV project, and you're not sure which
+libraries and tools you'll need, [KerasCV](https://keras.io/keras_cv/) is a good
+place to start. KerasCV is a library of modular CV components built on Keras
+Core. KerasCV includes models, layers, metrics, callbacks, and other tools that
+extend the high-level Keras API for CV tasks. The KerasCV APIs can help with
+data augmentation, classification, object detection, segmentation,
+image generation, and other common CV workflows. You can use KerasCV to quickly
+assemble production-grade, state-of-the-art training and inference pipelines.
+
+### Keras utilities
+
+`tf.keras.utils` provides several high-level image preprocessing utilities. For
+example, `tf.keras.utils.image_dataset_from_directory` generates a
+`tf.data.Dataset` from a directory of images on disk.
+
+### `tf.image`
+
+If KerasCV doesn't fit your use case, you can use `tf.image` and `tf.data` to
+write your own data augmentation pipelines or layers.
+
+The `tf.image` module contains various functions for image processing, such as
+`tf.image.flip_left_right`, `tf.image.rgb_to_grayscale`,
+`tf.image.adjust_brightness`, `tf.image.central_crop`, and
+`tf.image.stateless_random*`.
+
+The `tf.data` API enables you to build complex input pipelines from simple,
+reusable pieces.
+
+### TensorFlow Datasets
+
+[TensorFlow Datasets](https://www.tensorflow.org/datasets) is a collection of
+datasets ready to use with TensorFlow. Many of the datasets (for example,
+[MNIST](https://www.tensorflow.org/datasets/catalog/mnist),
+[Fashion-MNIST](https://www.tensorflow.org/datasets/catalog/fashion_mnist), and
+[TF Flowers](https://www.tensorflow.org/datasets/catalog/tf_flowers)) can be
+used to develop and test computer vision algorithms.
+
+## Where to start
+
+The following resources will help you get up and running with TensorFlow and
+Keras CV tools.
+
+* [KerasCV](https://keras.io/keras_cv/): Documentation and resources for
+  KerasCV.
+* [KerasCV developer guides](https://keras.io/guides/keras_cv/): Guides to
+  performing common CV tasks using KerasCV. If you're new to KerasCV,
+  [Classification with KerasCV](https://keras.io/guides/keras_cv/classification_with_keras_cv/)
+  is a good place to start.
+* [TensorFlow tutorials](https://www.tensorflow.org/tutorials): The core
+  TensorFlow documentation (this guide) includes a number of CV and image
+  processing tutorials.
+  * [Basic classification: Classify images of clothing](https://www.tensorflow.org/tutorials/keras/classification):
+    Train a neural network model to classify images of clothing, like sneakers
+    and shirts.
+  * [Load and preprocess images](https://www.tensorflow.org/tutorials/load_data/images):
+    Load and preprocess an image dataset in three ways:
+
+      1. Use high-level Keras preprocessing utilities to read a directory of
+         images on disk.
+      2. Write your own input pipeline from scratch
+         [using `tf.data`](https://www.tensorflow.org/guide/data).
+      3. Download a dataset from the large
+         [catalog](https://www.tensorflow.org/datasets/catalog/overview)
+         available in
+         [TensorFlow Datasets](https://www.tensorflow.org/datasets).
+  
+  * [Load video data](https://www.tensorflow.org/tutorials/load_data/video):
+    Load and preprocess AVI video data using the
+    [UCF101 human action dataset](https://www.tensorflow.org/datasets/catalog/ucf101).
+  * [Convolutional Neural Network (CNN)](https://www.tensorflow.org/tutorials/images/cnn):
+    Train a simple [Convolutional Neural Network](https://developers.google.com/machine-learning/glossary/#convolutional_neural_network)
+    (CNN) to classify
+    [CIFAR images](https://www.cs.toronto.edu/~kriz/cifar.html)
+    using the
+    [Keras API](https://www.tensorflow.org/guide/keras/overview).
+  * [Image classification](https://www.tensorflow.org/tutorials/images/classification):
+    Classify images of flowers using a `tf.keras.Sequential` model and load data
+    using `tf.keras.utils.image_dataset_from_directory`.
+  * [Transfer learning and fine-tuning](https://www.tensorflow.org/tutorials/images/transfer_learning):
+    Classify images of cats and dogs by using transfer learning from a
+    pre-trained network.
+  * [Data augmentation](https://www.tensorflow.org/tutorials/images/data_augmentation):
+    Increase the diversity of your training set by applying random (but
+    realistic) transformations, such as image rotation.
+  * [Image segmentation](https://www.tensorflow.org/tutorials/images/segmentation):
+    Perform image segmentation, using a modified
+    [U-Net](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/){: .external}.
+  * [Video classification with a 3D convolutional neural network](https://www.tensorflow.org/tutorials/video/video_classification):
+    Train a 3D convolutional neural network (CNN) for video classification using
+    the [UCF101](https://www.crcv.ucf.edu/data/UCF101.php){: .external} action
+    recognition dataset.
+  * [Transfer learning for video classification with MoViNet](https://www.tensorflow.org/tutorials/video/transfer_learning_with_movinet):
+    Use a pre-trained MoViNet model and the
+    [UCF101 dataset](https://www.crcv.ucf.edu/data/UCF101.php){: .external} to
+    classify videos for an action recognition task.
+