Getting started with Amazon Rekognition Custom Labels
Before starting these Getting started instructions, we recommend that you read Understanding Amazon Rekognition Custom Labels.
You use Amazon Rekognition Custom Labels to train a machine learning model. The trained model analyzes images to find the objects, scenes, and concepts that are unique to your business needs. For example, you can train a model to classify images of houses, or find the location of electronic parts on a printed circuit board.
To help you get started, Amazon Rekognition Custom Labels includes tutorial videos and example projects.
Note
For information about the AWS Regions and endpoints that Amazon Rekognition Custom Labels supports, see Rekognition endpoints and quotas.
Tutorial videos
The videos show you how to use Amazon Rekognition Custom Labels to train and use a model.
To view the tutorial videos
Sign in to the AWS Management Console and open the Amazon Rekognition console at https://console.aws.amazon.com/rekognition/
. In the left pane, choose Use Custom Labels. The Amazon Rekognition Custom Labels landing page is shown. If you don't see Use Custom Labels, check that the AWS Region you are using supports Amazon Rekognition Custom Labels.
In the navigation pane, choose Get started.
In What is Amazon Rekognition Custom Labels?, choose the video to watch the overview video.
In the navigation pane, choose Tutorials.
On the Tutorials page, choose the tutorial videos that you want to watch.
Example projects
Amazon Rekognition Custom Labels provides the following example projects.
Image classification
The image classification project (Rooms) trains a model that finds one or more household locations in an image, such as backyard, kitchen, and patio. The training and test images represent a single location. Each image is labeled with a single image-level label, such as kitchen, patio, or living_space. For an analyzed image, the trained model returns one or more matching labels from the set of image-level labels used for training. For example, the model might find the label living_space in the following image. For more information, see Find objects, scenes, and concepts.
Multi-label image classification
The multi-label image classification project (Flowers) trains a model that categorizes images of flowers into three concepts (flower type, leaf presence, and growth stage).
The training and test images have image-level labels for each concept, such as camellia for a flower type, with_leaves for a flower with leaves, and fully_grown for a flower that is fully grown.
For an analyzed image, the trained model returns matching labels from the set of image-level labels used for training. For example, the model returns the labels mediterranean_spurge and with_leaves for the following image. For more information, see Find objects, scenes, and concepts.
Brand detection
The brand detection project (Logos) trains a model that model finds the location of certain AWS logos such as Amazon Textract, and AWS lambda. The training images are of the logo only and have a single image level-label, such as lambda or textract. It is also possible to train a brand detection model with training images that have bounding boxes for brand locations. The test images have labeled bounding boxes that represent the location of logos in natural locations, such as an architectural diagram. The trained model finds the logos and returns a labeled bounding box for each logo found. For more information, see Find brand locations.
Object localization
The object localization project (Circuit boards) trains a model that finds the location of parts on a printed circuit board, such as a comparator or an infra red light emitting diode. The training and test images include bounding boxes that surround the circuit board parts and a label that identifies the part within the bounding box. In the following example image, the label names are ir_phototransistor, ir_led, pot_resistor, and comparator. The trained model finds the circuit board parts and returns a labeled bounding for each circuit part found. For more information, see Find object locations.
Using the example projects
These Getting Started instructions show you how to train a model by using example projects that Amazon Rekognition Custom Labels creates for you. It also shows you how to start the model and use it to analyze an image.
Creating the example project
To get started, decide which project to use. For more information, see Step 1: Choose an example project.
Amazon Rekognition Custom Labels uses datasets to train and evaluate (test) a model. A dataset manages images and the labels that identify the contents of images. The example projects include a training dataset and a test dataset in which all images are labeled. You don't need to make any changes before training your model. The example projects show the two ways in which Amazon Rekognition Custom Labels uses labels to train different types of models.
image-level – The label identifies an object, scene, or concept that represents the entire image.
bounding box – The label identifies the contents of a bounding box. A bounding box is a set of image coordinates that surround an object in an image.
Later, when you create a project with your own images, you must create training and test datasets, and also label your images. For more information, see Decide your model type.
Training the model
After Amazon Rekognition Custom Labels creates the example project, you can train the model. For more information, see Step 2: Train your model. After training finishes, you normally evaluate the performance of the model. The images in the example dataset already create a high-performance model, and you don't need to evaluate the model before running the model. For more information, see Improving a trained Amazon Rekognition Custom Labels model.
Using the model
Next you start the model. For more information, see Step 3: Start your model.
After you start running your model, you can use it to analyze new images. For more information, see Step 4: Analyze an image with your model.
You are charged for the amount of time that your model runs. When you finish using the example model, you should stop the model. For more information, see Step 5: Stop your model.
Next steps
When you're ready, you can create your own projects. For more information, see Step 6: Next steps.
