Git Reviews

Amazon Rekognition Reviews

Git mentions (225)

• pyaction 4.31.0 Released

  This Docker image is designed to support implementing Github Actions With Python. As of version 4.0.0., it starts with The official python docker image as the base Which is a Debian OS. It specifically uses python:3-slim to keep the image size Down for faster loading of Github Actions that use pyaction. On top of the Base, we've installed curl, Gpg, git, and the GitHub CLI. We added curl and gpg because they Are... - Source: dev.to / about 20 hours ago

• Commonly Used Git Commands

  First things first, let's set up Git on your machine. If you haven't installed Git yet, you can download it from Git's official website. After installing, configure your Git with your username and email:. - Source: dev.to / 2 days ago

• Yes. You can deploy Nuxt on Firebase App Hosting (2024)

  Git and a GitHub account  (create an account if you don't already have one). - Source: dev.to / 17 days ago

• Version Control in VSCode: Integrating Git and GitHub

  Download Git: Visit the Git website and download the installer for your operating system. - Source: dev.to / 6 days ago

• Implementing Role Based Access Control in SvelteKit

  Git: Ensure you have Git installed on your machine. If not, you can download it from the Git website. - Source: dev.to / 10 days ago

Amazon Rekognition mentions (33)

• Afraid of outgrowing AWS Rekognition? Try YOLO in Lambda.

  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 / 3 months ago

• Amazon Rekognition Custom Labels for Bears

  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 / 11 months ago

• “During a gold rush, sell shovels.” What are the shovels in the AI rush?

  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: 11 months ago

• Serverless Facial Recognition Voting Application Using AWS Services

  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

• Amazon Prime Video playback features

  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