Open Source BSD Object Detection Models
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Object Detection Models for BSD
Browse free open source Object Detection Models and projects for BSD below. Use the toggles on the left to filter open source Object Detection Models by OS, license, language, programming language, and project status.
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Use the Filerev app to organize Google Drive and reduce storage costs.Use the Filerev app to organize Google Drive and reduce storage costs. The Duplicate File Finder quickly shows all duplicates in Google Drive without downloading all of your files. The Storage Analyzer lets you browse your folders by size in Google Drive. You can also view your Google Drive files in different categories such as: hidden / orphaned files, large files, empty files, empty folders, large folders, old files, temporary files, files by extension. Every category includes powerful filters and the ability to bulk delete your files in Google Drive. Plus, there are charts and graphs to help you quickly see how your storage space is being used and the number of files and specific types of files or folders that are consuming the most space. You can get started for free to see what is consuming your Google Drive storage space and quickly clean up the clutter.
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Venue management software for the growing attractionAt ROLLER, we're passionate about empowering leisure and entertainment businesses to reach new heights of success. With a track record of serving 1,500+ customers across 25 countries, including some of the most renowned names in the attractions industry, including SkyZone, Altitude, American Dream, Uptown Jungle, Flip Out, WhoaZone, Oxygen, Innoflate, and Jumpsquare, we understand the unique needs of playcenters, family entertainment centers, wake parks, water parks, trampoline parks, theme parks, amusement parks, indoor climbing facilities, children's museums, zoos, aquariums and more.
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MediaPipe Face Detection
Detect faces in an image
The MediaPipe Face Detection model is a high-performance, real-time face detection solution that uses machine learning to identify faces in images and video streams. It is optimized for mobile and embedded platforms, offering fast and accurate face detection while maintaining a small memory footprint. This model supports multiple face detections and is highly efficient, making it suitable for a variety of applications such as augmented reality, user authentication, and facial expression analysis. -
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Blazeface
Blazeface is a lightweight model that detects faces in images
Blazeface is a lightweight, high-performance face detection model designed for mobile and embedded devices, developed by TensorFlow. It is optimized for real-time face detection tasks and runs efficiently on mobile CPUs, ensuring minimal latency and power consumption. Blazeface is based on a fast architecture and uses deep learning techniques to detect faces with high accuracy, even in challenging conditions. It supports multiple face detection in varying lighting and poses, and is designed to work in real-world applications like mobile apps, robotics, and other resource-constrained environments.
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