The Amazon Lookout for Vision console provides a holistic view across all your production lines with an easy to use dashboard. The dashboard shows the projects by most defects, recent defects, and highest anomaly ratio, which enables you to quickly identify the production lines and processes that need immediate attention.
The Amazon Lookout for Vision console provides a visual interface to label your images quickly and simply. The interface allows you to apply a normal or anomaly label to the entire image with a simple click of a button. You can also select multiple images and apply the label to the entire selection with one click. Alternatively, if you have a large data set, you can use Amazon SageMaker Ground Truth to efficiently label your images at scale.
Evaluate your anomaly detection model’s performance on your test dataset. If you do not provide your own test dataset, Amazon Lookout for Vision can automatically create a test dataset for you to evaluate your model’s performance. For every image in the test dataset, you can see a side-by-side comparison of the model’s prediction vs. the label assigned. You can also review detailed performance metrics such as precision/recall metrics, ROC curve, and confidence scores.
Trial anomaly detection tasks and feedback
You can instantly run test detection tasks on additional images to get normal or anomaly predictions using your model. You can track your predictions, correct any mistakes, and provide the feedback to retrain newer models to improve anomaly detection accuracy.
Manufacturing line integration
You can integrate Amazon Lookout for Vision with your manufacturing lines and implement automated visual inspection workflows for your use cases with just a few clicks in the Amazon Lookout for Vision console and a few API parameters. The Amazon Lookout for Vision API also enables you to integrate your workflows into Amazon Augmented AI (Amazon A2I) for human review and verification by your process engineers so that they can continuously improve the accuracy of your models.
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