Gumlet Video streamlines the process of uploading, transcoding, optimizing, hosting, and analyzing videos, enabling rapid streaming to vast audiences. Gumlet Image Optimization enhances website speed, responsiveness, user experience (UX), and search engine optimization (SEO).
Gumlet is loved by 8000+ businesses and start-ups and delivers over 1.5 Billion media files daily. With an average optimization rate of 54%, Gumlet offers an exceptional video and media experience for users across websites and applications.
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Image loads fast, im impressed with the speed and ease of installation. I have successfully deployed them on afew of my websites and it improves my overall loading speed.
Gumlet is a wonderful little tool that I use for optimising your images and thus your website loading speeds. We use it for our work (and client projects) and it works very well!
Pros: - Inbuilt CDN - Easy integration with Wordpress - on-the-fly image manipulation is awesome - Supports CNAME now!
Cons: - Missing some smart (AI-like) image manipulation features that Cloudinary has like Smart/Face-cropping for example. Would love to have those!
Gumlet is easy to implement and instantly improves the performance of your site. I compared to other providers and was happy to see that it outperforms the competition
Based on our record, Amazon Rekognition seems to be more popular. It has been mentiond 33 times since March 2021. 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.
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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