Understanding Amazon Lookout for Vision - Amazon Lookout for Vision

Understanding Amazon Lookout for Vision

You can use Amazon Lookout for Vision to find visual defects in industrial products, accurately and at scale, for tasks such as:

  • Detecting damaged parts – Spot damage to a product’s surface quality, color, and shape during the fabrication and assembly process.

  • Identifying missing components – Determine missing components based on the absence, presence, or placements of objects. For example, a missing capacitor on a printed circuit board.

  • Uncovering process issues – Detect defects with repeating patterns, such as repeated scratches in the same spot on a silicon wafer.

With Lookout for Vision you create a computer vision model that predicts the presence of anomalies in an image. You provide the images that Amazon Lookout for Vision uses to train and test your model. Amazon Lookout for Vision provides metrics that you can use to evaluate and improve your trained model. You can host the trained model in the AWS cloud or you can deploy the model to an edge device. A simple API operation returns the predictions that your model makes.

The general workflow for creating, evaluating, and using a model is as follows:

Choose your model type

Before you can create a model, you must decide which type of model you want. You can create two types of model, image classification and image segmentation. You decide which type of model to create based on your use case.

Image classification model

If you only need to know if an image contains an anomaly, but don’t need to know its location, create an image classification model. An image classification model makes a prediction of whether an image contains an anomaly. The prediction includes the model's confidence in the accuracy of the prediction. The model doesn’t provide any information about the location of any anomalies found on the image.

Image segmentation model

If you need to know the location of an anomaly, such as the location of a scratch, create an image segmentation model. Amazon Lookout for Vision models use semantic segmentation to identify the pixels on an image where the types of anomalies (such as a scratch or a missing part) are present.

Note

A semantic segmentation model locates different types of anomaly. It doesn't provide instance information for individual anomalies. For example, if an image contains two dents, Lookout for Vision returns information about both dents in a single entity representing the dent anomaly type.

An Amazon Lookout for Vision segmentation model predicts the following:

Classification

The model returns a classification for an analyzed image (normal/anomaly), which includes the model's confidence in the prediction. Classification information is calculated separately from segmentation information and you shouldn't assume a relationship between them.

Segmentation

The model returns an image mask that marks the pixels where anomalies occur on the image. Different types of anomaly are color coded according to the color assigned to the anomaly label in the dataset. An anomaly label represents the type of an anomaly. For example, the blue mask in the following image marks the location of a scratch anomaly type found on a car.

The model returns the color code for each anomaly label in the mask. The model also returns the percentage covering of the image that an anomaly label has.

With a Lookout for Vision segmentation model, you can use various criteria to analyze the analysis results from the model. For example:

  • Anomaly location – If you need to know the location of anomalies, use segmentation information to see masks that cover anomalies.

  • Types of anomaly – Use segmentation information to decide if an image contains more than an acceptable number of anomaly types.

  • Area of coverage – Use segmentation information to decide if an anomaly type covers more than an acceptable area of an image.

  • Image classification – If you don't need to know the location of anomalies, use classification information to determine if an image contains anomalies.

For example code, see Detecting anomalies in an image.

After you decide which type of model you want, you create a project and a dataset to manage your model. Using Labels, you can classify images as normal or an anomaly. Labels also identify segmentation information such as masks and anomaly types. How you label the images in your dataset determines the type of model that Lookout for Vision creates for you.

Labeling an image segmentation model is more complex than labeling an image classification model. To train a segmentation model, you have to classify the training images as normal or anomalous. You also have to define anomaly masks and anomaly types for each anomalous image. A classification model only requires you to identify training images as normal or anomalous.

Create your model

The steps to create a model are creating a project, creating a dataset, and training the model are as follows:

Create a project

Create a project to manage the datasets and the models that you create. A project should be used for a single use case, such as detecting anomalies in a single type of machine part.

You can use the dashboard to get an overview of your projects. For more information, see Using the Amazon Lookout for Vision dashboard.

More information: Create your project.

Create a dataset

To train a model Amazon Lookout for Vision needs images of normal and anomalous objects for your use case. You supply these images in a dataset.

A dataset is a set of images and labels that describe those images. The images should represent a single type of object on which anomalies can occur. For more information, see Preparing images for a dataset.

With Amazon Lookout for Vision you can have a project that uses a single dataset, or a project that has separate training and test datasets. We recommend using a single dataset project unless you want finer control over training, testing, and performance tuning.

You create a dataset by importing the images. Depending on how you import the images, the images might be also be labeled. If not, you use the console to label the images.

Importing images

If you create the dataset with the Lookout for Vision console, you can import the images in one of the following ways:

  • Import images from your local computer. The images aren't labeled.

  • Import images from an S3 bucket. Amazon Lookout for Vision can classify the images using the folder names that contain the images. Use normal for normal images. Use anomaly for anomalous images. You can't automatically assign segmentation labels.

  • Import an Amazon SageMaker Ground Truth manifest file. Images in a manifest file are labeled. You can create and import your own manifest file. If you have many images, consider using the SageMaker Ground Truth labeling service. You then import the output manifest file from the Amazon SageMaker Ground Truth job.

Labeling images

Labels describe an image in a dataset. Labels specify if an image is normal or anomalous (classification). Labels also describe the location of anomalies on an image (segmentation).

If your images aren't labeled, you can use the console to label them.

The labels you assign to images in your dataset determines the type of model that Lookout for Vision creates:

Image classification

To create an image classification model, use the Lookout for Vision console to classify images in the dataset as normal or an anomaly.

You can also use the CreateDataset operation to create a dataset from a manifest file that includes classification information.

Image segmentation

To create an image segmentation model, use the Lookout for Vision console to classify images in the dataset as normal or an anomaly. You also specify pixel masks for anomalous areas on the image (if they exist) as well as an anomaly label for individual anomaly masks.

You can also use the CreateDataset operation to create a dataset from a manifest file that includes segmentation and classification information.

If your project has separate training and test datasets, Lookout for Vision uses the training dataset to learn and determine the model type. You should label the images in your test dataset in the same way.

More information: Creating your dataset.

Train your model

Training creates a model and trains it to predict the presence of anomalies in images. A new version of your model is created each time you train.

At the start of training, Amazon Lookout for Vision chooses the most suitable algorithm to train your model with. The model is trained and then tested. When you train a single dataset project, the dataset is internally split to create a training dataset and a test dataset. In Getting Started, your project has separate training and test datasets. In this configuration, Amazon Lookout for Vision trains your model with the training dataset and tests the model with the test dataset.

Important

You are charged for the amount of time that it takes to successfully train your model.

More information: Train your model.

Evaluate your model

Evaluate the performance of your model by using the performance metrics created during testing.

Using performance metrics, you can better understand the performance of your trained model, and decide if you're ready to use it in production.

More information: Improving your model.

If the performance metrics indicate that improvements are needed, you can add more training data by running a trial detection task with new images. After the task completes, you can verify the results and add the verified images to your training dataset. Alternatively, you can add new training images directly to the dataset. Next, you retrain your model and recheck the performance metrics.

More information: Verifying your model with a trial detection task .

Use your model

Before you can use your model in the AWS cloud, you start the model with the StartModel operation. You can get the StartModel CLI command for your model from the console.

More information: Start your model.

A trained Amazon Lookout for Vision model predicts whether an input image contains normal or anomalous content. If your model is a segmentation model, the prediction includes an anomaly mask that marks the pixels where anomalies are found.

To make a prediction with your model, call the DetectAnomalies operation and pass an input image from your local computer. You can get the CLI command that calls DetectAnomalies from the console.

More information: Detect anomalies in an image.

Important

You are charged for the time that your model is running.

If you are no longer using your model, use the StopModel operation to stop the model. You can get the CLI command from the console.

More information: Stop your model.

Use your model on an edge device

You can use a Lookout for Vision model on an AWS IoT Greengrass Version 2 core device.

More information: Using your Amazon Lookout for Vision model on an edge device.

Use your dashboard

You can use the dashboard to get an overview of all your projects and overview information for individual projects.

More information: Use your dashboard.