pngquant VS Amazon Rekognition

pngquant Reviews

Amazon Rekognition Reviews

pngquant mentions (28)

• Random Code Inspiration Volume 2

  Image-shrinker is a simple, easy to use open source tool for shrinking images. Under the hood it uses pngquant, mozjpg, SVGO, and gifsicle. You can also install these tools individually if you need to compress some images. I often use pngquantafter exporting PNGs for web projects from Figma or similar tools. I literally run it like this:. - Source: dev.to / 8 months ago

• keeping collection size down (for ankiweb) when needing lots of image cards/notes

  Searching more I found https://pngquant.org/ which I could add to my bulk workflow to make most png's approach the jpeg size. Source: 12 months ago

• screen capture/snapshot utility with image optimization support/configurability

  But this did prompt me to do some searching, and I see https://pngquant.org/ which seems to achieve jpeg like size reduction while maintaining the file as a png. One difference they note is that this method will typically preserve sharp edges better than jpeg (which is probably a strong plus for my type of use case). Source: 12 months ago

• Exported png image with color-to-alpha edit is huge

  Pngquant is also great for shaving filesizes down, but unlike oxipng, it's explicitly lossy. It'll reduce colors and even dither, but it will try to keep an image visually similar. Https://pngquant.org/. Source: over 1 year ago

• Name a program that doesn't get enough love!

  Oxipng, pngquant and svgcleaner — optimizing images. Source: over 1 year 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 / about 1 month 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 / 9 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: 10 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 / almost 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