The 10 Most Popular Computer Vision (11k) Libraries in 2023
As the field of computer vision continues to grow and evolve, there are a wide range of libraries available to help data scientists and software developers build powerful computer vision applications. In this post, we’ll take a closer look at the 10 most popular computer vision libraries, based on their popularity among data scientists and developers. You can run these libraries for your computer vision workflows on Saturn Cloud for free.
- OpenCV
OpenCV is one of the most widely used computer vision libraries, and for good reason. It’s an open-source library that provides a wide range of tools and algorithms for image and video processing, object detection, feature extraction, and more. With support for multiple programming languages, including C++, Python, and Java, OpenCV is a versatile library that can be used in a wide range of applications.
- TensorFlow
TensorFlow is a popular library for machine learning, but it also includes a range of tools for computer vision. With its powerful neural network capabilities, TensorFlow can be used for tasks such as image classification, object detection, and image segmentation. TensorFlow also includes pre-trained models that can be used to quickly get started with computer vision applications.
- Keras
Keras is a high-level neural network library that can be used for a range of applications, including computer vision. With Keras, developers can quickly build and train neural networks for tasks such as image classification, object detection, and more. Keras is known for its simplicity and ease of use, making it a popular choice for many developers.
- Dlib
Dlib is a C++ library that provides a range of tools for machine learning and computer vision. With Dlib, developers can build applications for tasks such as face detection, object tracking, and more. Dlib is known for its speed and efficiency, making it a popular choice for applications that require real-time processing.
- Scikit-image
Scikit-image is a Python library that provides a range of tools for image processing and computer vision. With Scikit-image, developers can perform tasks such as image segmentation, feature extraction, and more. Scikit-image is known for its ease of use and flexibility, making it a popular choice for many developers.
- Caffe
Caffe is a deep learning library that can be used for a range of applications, including computer vision. With Caffe, developers can build and train neural networks for tasks such as image classification, object detection, and more. Caffe is known for its speed and efficiency, making it a popular choice for applications that require real-time processing.
- Torchvision
Torchvision is a computer vision library that is built on top of PyTorch. With Torchvision, developers can perform tasks such as object detection, image segmentation, and more. Torchvision includes pre-trained models that can be used to quickly get started with computer vision applications.
- Mahotas
Mahotas is a Python library that provides a range of tools for image processing and computer vision. With Mahotas, developers can perform tasks such as image segmentation, feature extraction, and more. Mahotas is known for its speed and efficiency, making it a popular choice for many developers.
- SimpleCV
SimpleCV is a Python library that provides a range of tools for computer vision. With SimpleCV, developers can perform tasks such as object detection, image segmentation, and more. SimpleCV is known for its simplicity and ease of use, making it a popular choice for many developers.
- VIT(vision in transformer)
Vision Transformer, also known as ViT, was one of the pioneering works that applied transformers to computer vision. ViT divides an image into patches and treats them as sequences, enabling transformers to process the visual information. It achieves impressive results on image classification tasks and has been widely adopted as a baseline for transformer-based computer vision models.
In conclusion, there are a wide range of computer vision libraries available to data scientists and software developers. Whether you’re looking for a powerful neural network library or a simple Python library for image processing, there’s a library out there that can meet your needs. By exploring the 10 most popular computer vision libraries, you can find the right tools to build powerful computer vision applications.
