Showing 2 open source projects for "computer vision"

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  • Build Data Resilience - Take the Assessment Today Icon
    Build Data Resilience - Take the Assessment Today

    Can you recover when it matters most? Take this quick assessment to identify gaps and build greater recovery confidence.

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  • Paessler: Easy to Use With Enterprise Power. Free Trial Icon
    Paessler: Easy to Use With Enterprise Power. Free Trial

    A low-code dashboard makes monitoring intuitive for any admin, while scripting and custom sensors give experts full control.

    You shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
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    Super comprehensive deep learning notes

    Super comprehensive deep learning notes

    Super Comprehensive Deep Learning Notes

    Super comprehensive deep learning notes is a massive and well-structured collection of deep learning notebooks that serve as a comprehensive study resource for anyone wanting to learn or reinforce concepts in computer vision, natural language processing, deep learning architectures, and even large-model agents. The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural network designs like ResNet, transformers, and object detection algorithms. ...
    Downloads: 0 This Week
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    CIApp

    CIApp

    CIApp is an innovative application designed to track and count reps

    Automatic Repetition Counting: Detects and counts exercise repetitions (e.g., pull-ups, push-ups, squats) in real-time using AI-powered motion tracking. Reduces the need for manual counting, allowing users to focus fully on their workout. Advanced Computer Vision: Utilizes cutting-edge machine learning models to recognize movements and distinguish between exercise types. Works with both webcams and video uploads for ultimate flexibility. User-Friendly Interface: Simple and intuitive design for users of all fitness levels. Displays real-time feedback, including exercise count, duration, and form quality. ...
    Downloads: 0 This Week
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