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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 / 8 days ago
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 / 5 months ago
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
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
There's probably something in scikit-image to do what you want, or close enough to build on. Source: about 2 years ago
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
OpenCV - OpenCV is the world's biggest computer vision library
machine-learning in Python - Do you want to do machine learning using Python, but you’re having trouble getting started? In this post, you will complete your first machine learning project using Python.
Microsoft Computer Vision API - Extract rich information from images and analyze content with Computer Vision, an Azure Cognitive Service.
Google Cloud TPU - Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.
Amazon Rekognition - Add Amazon's advanced image analysis to your applications.