Showing 4 open source projects for "split image"

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  • 1
    Generative AI for Beginners (Version 3)

    Generative AI for Beginners (Version 3)

    21 Lessons, Get Started Building with Generative AI

    Generative AI for Beginners is a 21-lesson course by Microsoft Cloud Advocates that teaches the fundamentals of building generative AI applications in a practical, project-oriented way. Lessons are split into “Learn” modules for core concepts and “Build” modules with hands-on code in Python and TypeScript, so you can jump in at any point that matches your goals. The course covers everything from model selection, prompt engineering, and chat/text/image app patterns to secure development practices and UX for AI. It also walks through modern application techniques such as function calling, RAG with vector databases, working with open source models, agents, fine-tuning, and using SLMs. ...
    Downloads: 5 This Week
    Last Update:
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  • 2
    ZML

    ZML

    Any model. Any hardware. Zero compromise

    ...One of its key strengths is cross-compilation, enabling developers to build once and deploy across various platforms without rewriting code. zml provides example implementations of models and workflows, demonstrating how to run inference tasks such as image classification or large language models. It is designed to handle complex distributed setups, including scenarios where model components are split across devices connected via networks.
    Downloads: 0 This Week
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  • 3
    qiji-font

    qiji-font

    Typeface from Ming Dynasty woodblock printed books

    ...Next, the image is thresholded and fed into the contour-tracing algorithm. A metric is then used to discard shapes that are unlikely to be part of the character in interest.
    Downloads: 0 This Week
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  • 4
    VGGFace2

    VGGFace2

    VGGFace2 Dataset for Face Recognition

    VGGFace2 is a large-scale face recognition dataset developed to support research on facial recognition across variations in pose, age, illumination, and identity. It consists of 3.31 million images covering 9,131 subjects, with an average of over 360 images per subject. The dataset was collected from Google Image Search, ensuring a wide diversity in ethnicity, profession, and real-world conditions. It is split into a training set with 8,631 identities and a test set with 500 identities, making it suitable for benchmarking and large-scale model training. Alongside the dataset, the repository provides pre-trained models based on ResNet-50 and SE-ResNet-50 architectures, trained with both MS-Celeb-1M pretraining and fine-tuning on VGGFace2. ...
    Downloads: 15 This Week
    Last Update:
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