Showing 4 open source projects for "development images"

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  • 1
    MedGemma

    MedGemma

    Collection of Gemma 3 variants that are trained for performance

    MedGemma is a collection of specialized open-source AI models created by Google as part of its Health AI Developer Foundations initiative, built on the Gemma 3 family of transformer models and trained for medical text and image comprehension tasks that help accelerate the development of healthcare-focused AI applications. It includes multiple variants such as a 4 billion-parameter multimodal model that can process both medical images and text and a 27 billion-parameter text-only (and multimodal) model that offers deeper clinical reasoning and understanding at higher capacity, making it suitable for complex tasks like medical question answering, summarization of clinical notes, or generating reports from radiology images. ...
    Downloads: 2 This Week
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  • 2
    CO3D (Common Objects in 3D)

    CO3D (Common Objects in 3D)

    Tooling for the Common Objects In 3D dataset

    CO3Dv2 (Common Objects in 3D, version 2) is a large-scale 3D computer vision dataset and toolkit from Facebook Research designed for training and evaluating category-level 3D reconstruction methods using real-world data. It builds upon the original CO3Dv1 dataset, expanding both scale and quality—featuring 2× more sequences and 4× more frames, with improved image fidelity, more accurate segmentation masks, and enhanced annotations for object-centric 3D reconstruction. CO3Dv2 enables research...
    Downloads: 0 This Week
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  • 3
    Improved Diffusion

    Improved Diffusion

    Release for Improved Denoising Diffusion Probabilistic Models

    ...By making this code available, OpenAI provides a foundation for further experimentation and development in generative modeling research.
    Downloads: 7 This Week
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  • 4
    InfoGAN

    InfoGAN

    Code for reproducing key results in the paper

    ...That extra incentive encourages the generator to structure its latent space in a way where certain latent variables control meaningful, distinct factors (e.g. rotation, width, stroke thickness) in the output images. The repository includes code for experiments (e.g. on MNIST), launcher scripts, and some tests. It depends on a development version of TensorFlow (the code expects features not in older stable releases), and also uses other libraries like prettytensor and progressbar.
    Downloads: 0 This Week
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