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Awesome GitHub RepositoriesImage Dataset Preparers

Tools for transforming raw image collections into standardized formats for deep learning training.

Distinct from Machine Learning Data Preparation: Distinct from general machine learning data preparation: specifically focuses on image-based scaling and cropping workflows.

Explore 3 awesome GitHub repositories matching data & databases · Image Dataset Preparers. Refine with filters or upvote what's useful.

Awesome Image Dataset Preparers GitHub Repositories

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  • openai/improved-diffusionopenai 的头像

    openai/improved-diffusion

    3,829在 GitHub 上查看↗

    This project is a diffusion model framework for training and sampling from denoising probabilistic models to generate images from noise. It functions as a generative image model that creates visual content by iteratively refining random noise into coherent images. The system includes a distributed GPU trainer designed to scale complex neural network architectures across multiple graphics processing units. It also provides an image dataset preprocessor to prepare, scale, and standardize raw image collections for training. The framework covers model training and image generation, utilizing noi

    Includes tools for preparing raw image collections into standardized formats through scaling and label extraction.

    Python
    在 GitHub 上查看↗3,829
  • trickygo/dive-into-dl-tensorflow2.0TrickyGo 的头像

    TrickyGo/Dive-into-DL-TensorFlow2.0

    3,826在 GitHub 上查看↗

    This project is a structured TensorFlow deep learning curriculum and an interactive machine learning course delivered through Jupyter Notebooks. It serves as a technical guide and model zoo providing reference implementations for neural networks and machine learning algorithms. The curriculum focuses on practical implementations of computer vision, including object detection, semantic segmentation, and style transfer. It also provides tutorials for natural language processing, specifically covering word embeddings and encoder-decoder architectures for sequence modeling. The material covers t

    Transforms raw image collections into shuffled, batched streams for deep learning training.

    Jupyter Notebookbookchinese-simplifiedcv
    在 GitHub 上查看↗3,826
  • christianversloot/machine-learning-articleschristianversloot 的头像

    christianversloot/machine-learning-articles

    3,683在 GitHub 上查看↗

    This project is a machine learning educational archive and technical documentation collection. It serves as a deep learning tutorial series and implementation guide, providing theoretical explanations and practical walkthroughs for constructing and optimizing neural networks. The content focuses on the design and construction of diverse model architectures, including convolutional neural networks, Long Short-Term Memory networks, and generative adversarial networks. It details specific implementation patterns for autoencoders, sentiment analysis models, and various classification approaches.

    Walks through the process of normalizing and formatting image datasets for deep learning compatibility.

    albertbertclustering
    在 GitHub 上查看↗3,683
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