Based on our record, Flutter.dev seems to be a lot more popular than Amazon Rekognition. While we know about 341 links to Flutter.dev, we've tracked only 33 mentions of Amazon Rekognition. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Deploying Dart functions to AWS Lambda enables you to utilize them not only within AWS Lambda but also integrate them with services like Amazon API Gateway, allowing you to leverage them in Flutter applications as well. This unified codebase in Dart offers great convenience. - Source: dev.to / 1 day ago
If you are considering Electron/React then I would suggest adding Flutter to your list of technologies to consider. It uses Dart (a language similar to C#) and has a lot going for it… relatively quick to get up to speed with, fantastic developer experience (e.g., hot reload, great IDE support, good development tools) and very strong cross-platform support: it generates native iOS, Android, MacOS, Windows and Linux... - Source: Hacker News / about 1 month ago
You can find the React Native documentation here and Flutter Documentation here. - Source: dev.to / about 2 months ago
Download the Flutter SDK: Visit the Flutter official website (https://flutter.dev/), click "Get Started", select the download link suitable for your operating system, and download the Flutter SDK zip file. - Source: dev.to / 3 months ago
Flutter: Google's UI toolkit that can compile to iOS and Android platforms from a single codebase. - Source: dev.to / 4 months ago
AWS Rekognition is a great choice for many types of real-world projects or just for testing an idea on your images. The issue eventually comes with its cost, unfortunately, which we will see later in a specific example. Don’t get me wrong, Rekognition is a great service and I love to use it for its simplicity and reliable performance on quite a few projects. - Source: dev.to / about 1 month ago
I don’t really want to spend so much time manually adjusting labels. For most machine learning, the next step would be to fine tune your model. You can essentially fine tune Amazon Rekognition by using Custom Labels. You can do this to make it better at detecting specific objects (like bears) or train it to detect new objects like your product or logo. It really depends on your application needs. - Source: dev.to / 10 months ago
For instance, are you a company with lots of security cameras? Hire me to write a program that pipes your data into AWS rekognition and then shows you a dashboard of what happened on your cams today. Got a ton of products with no meta-description? Hire me to write a program that pipes your data into OpenAI, and then saves the generated description to your custom CMS. Source: 10 months ago
Amazon Rekognition: Used to index, detect faces in the picture, and compare faces when users try voting, it was the heart of the facial voting feature. - Source: dev.to / about 1 year ago
Sure. But if you think generating thumbnails and detecting intros/credits takes a long time, wait until your computer is running machine learning/computer vision over your entire library. They also have to build and train that model which is no trivial task. And I know what you're thinking, why don't they just use Amazon's Rekognition service that does celebrity identification? Well, it's $0.10 per minute of... Source: about 1 year ago
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