Introducing the computervision-recipes Project
Computer vision refers to the ability of machines to interpret and make decisions based on visual data. This field has grown tremendously with applications in various domains like face recognition, image understanding, and autonomous vehicles. Central to these applications are tasks such as image classification, object detection, and image similarity recognition.
About computervision-recipes
The computervision-recipes project provides a comprehensive set of tools and examples designed to develop computer vision systems. The focus is on leveraging recent advances in Computer Vision algorithms and neural networks to create practical, ready-to-use solutions. Rather than building from scratch, the project builds upon state-of-the-art libraries, optimizing and scaling applications to cloud environments.
The project offers solutions that aim to significantly reduce the time required to bring a computer vision solution to market by addressing common questions, pitfalls, and best practices. Through a wide array of examples and utilities written in Jupyter notebooks, users can quickly get hands-on with various computer vision scenarios. The notebooks use PyTorch as the primary deep learning library.
Features of the Project
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Action Recognition and Tracking: The recent update has included support for recognizing and tracking actions within video sequences, showcasing the detailed capabilities of this repository.
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Target Users: The repository is ideal for data scientists and machine learning engineers, irrespective of their level of knowledge in computer vision. It particularly benefits those looking for custom machine learning modeling solutions.
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Getting Started: Users are advised to start with the setup guide to prepare the necessary computing environment. The project recognizes the importance of foundational knowledge and recommends starting with image classification tasks.
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Easy Access via Binder: If local setup is a hurdle, users can easily access and experiment with the notebooks via Binder, although note that it provides limited computational power.
Core Scenarios Covered
The repository explores multiple computer vision scenarios:
- Classification: Identifying the category of a given image.
- Similarity: Computing how similar two images are.
- Detection: Detecting and drawing bounding boxes around objects in an image.
- Keypoint Detection: Identifying specific points on an object, particularly useful for human pose estimation.
- Segmentation: Classifying each pixel in an image.
- Action Recognition: Identifying actions in videos.
- Tracking: Detecting and following objects across video frames.
- Crowd Counting: Estimating the number of individuals in crowded settings.
The scenarios and code are structured into base (well-tested) and contrib (cutting-edge) categories, allowing users to select based on reliability or innovation needs.
Integration with Azure
For those not needing custom models, Azure offers robust computer vision solutions with minimal coding requirements. Microsoft's Vision Services offer pre-trained APIs for various computer vision tasks, while the Custom Vision service allows users to train models on their data with ease. Azure Machine Learning services further facilitate scalable model training and deployment.
Contributing and Support
The project welcomes community contributions, and guidelines are provided for those interested in participating in its development.
The computervision-recipes project stands as a gateway for innovators in computer vision, providing the tools, examples, and guidance needed to create impactful visual solutions while simplifying the journey from concept to deployment.