Scikit Image VS Amazon Rekognition

Scikit Image Reviews

Amazon Rekognition Reviews

Scikit Image mentions (7)

• How to Estimate Depth from a Single Image

  We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics. - Source: dev.to / about 2 hours ago

• Exploring Open-Source Alternatives to Landing AI for Robust MLOps

  Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 4 months ago

• Is it possible to add a noise to an image in python?

  This is a good cv deep learning book with python examples https://www.manning.com/books/deep-learning-for-vision-systems. If you're pretty comfortable with the concepts of traditional image processing this is a good companion to cv2 (so you don't have to reinvent the wheel) https://scikit-image.org/. Source: over 1 year ago

• A CLI that does simple image processing and also generates cool patterns

  Also, don't know if you're familiar with Python, but if you need ideas for to implement for future directions : https://scikit-image.org/. Source: over 1 year ago

• Color Matrices for scan correction

  There's probably something in scikit-image to do what you want, or close enough to build on. Source: almost 2 years 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 / 12 months 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