Showing 14 open source projects for "image processing framework"

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  • 1
    PaddleNLP

    PaddleNLP

    Easy-to-use and powerful NLP library with Awesome model zoo

    PaddleNLP It is a natural language processing development library for flying paddles, with Easy-to-use text area API, Examples of applications for multiple scenarios, and High-performance distributed training Three major features, aimed at improving the modeling efficiency of the flying oar developer's text field, aiming to improve the developer's development efficiency in the text field, and provide rich examples of NLP applications. Provide rich industry-level pre-task capabilities...
    Downloads: 1 This Week
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  • 2
    SentenceTransformers

    SentenceTransformers

    Multilingual sentence & image embeddings with BERT

    SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning.
    Downloads: 0 This Week
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  • 3
    Haystack

    Haystack

    Haystack is an open source NLP framework to interact with your data

    Apply the latest NLP technology to your own data with the use of Haystack's pipeline architecture. Implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications. Evaluate components and fine-tune models. Ask questions in natural language and find granular answers in your documents using the latest QA models with the help of Haystack pipelines. Perform semantic search and retrieve ranked documents according to meaning,...
    Downloads: 4 This Week
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  • 4
    Megatron

    Megatron

    Ongoing research training transformer models at scale

    ...We developed efficient, model-parallel (tensor, sequence, and pipeline), and multi-node pre-training of transformer based models such as GPT, BERT, and T5 using mixed precision. Megatron is also used in NeMo Megatron, a framework to help enterprises overcome the challenges of building and training sophisticated natural language processing models with billions and trillions of parameters. Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
    Downloads: 2 This Week
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  • 5
    Dream Textures

    Dream Textures

    Stable Diffusion built-in to Blender

    ...Outpaint to increase the size of an image by extending it in any direction. Perform style transfer and create novel animations with Stable Diffusion as a post processing step. Dream Textures has been tested with CUDA and Apple Silicon GPUs. Over 4GB of VRAM is recommended.
    Downloads: 3 This Week
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  • 6
    Simple StyleGan2 for Pytorch

    Simple StyleGan2 for Pytorch

    Simplest working implementation of Stylegan2

    Simple Pytorch implementation of Stylegan2 that can be completely trained from the command-line, no coding needed. You will need a machine with a GPU and CUDA installed. You can also specify the location where intermediate results and model checkpoints should be stored. You can increase the network capacity (which defaults to 16) to improve generation results, at the cost of more memory. By default, if the training gets cut off, it will automatically resume from the last checkpointed file....
    Downloads: 0 This Week
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  • 7
    MMGeneration

    MMGeneration

    MMGeneration is a powerful toolkit for generative models

    ...MMGeneration is a powerful toolkit for generative models, especially for GANs now. It is based on PyTorch and MMCV. The master branch works with PyTorch 1.5+. We currently support training on Unconditional GANs, Internal GANs, and Image Translation Models. Support for conditional models will come soon. A plentiful toolkit containing multiple applications in GANs is provided to users. GAN interpolation, GAN projection, and GAN manipulations are integrated into our framework. It's time to play with your GANs! For the highly dynamic training in generative models, we adopt a new way to train dynamic models with MMDDP. ...
    Downloads: 0 This Week
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  • 8
    Yodd's AI Chat

    Yodd's AI Chat

    This app uses the OpenAISwift library, ChatGPTSwift library and OpenAI

    Welcome to Yodd AI Chat, your new AI-powered chatbot companion! Yodd AI Chat uses the latest OpenAI API to provide you with an unparalleled chat experience. Whether you're looking for a friendly conversation or need some help with a problem, Yodd AI Chat is here to help. Free chatbot app for OpenAI's ChatGPT. Yodd's ChatGPT is a free and open-source implementation of the OpenAI API in Swift for iOS. It uses the OpenAISwift framework, ChatGPTSwift framework and OpenAI framework. Apparenlty...
    Downloads: 0 This Week
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  • 9
    Data augmentation

    Data augmentation

    List of useful data augmentation resources

    List of useful data augmentation resources. You will find here some links to more or less popular github repos, libraries, papers, and other information. Data augmentation can be simply described as any method that makes our dataset larger. To create more images for example, we could zoom in and save a result, we could change the brightness of the image or rotate it. To get a bigger sound dataset we could try to raise or lower the pitch of the audio sample or slow down/speed up....
    Downloads: 1 This Week
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  • 10
    Old Photo Restoration

    Old Photo Restoration

    Bringing Old Photo Back to Life (CVPR 2020 oral)

    We propose to restore old photos that suffer from severe degradation through a deep learning approach. Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize. Therefore, we propose a novel triplet domain translation network by leveraging real photos along with massive synthetic image pairs. Specifically, we train two...
    Downloads: 4 This Week
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  • 11
    Deep Exemplar-based Video Colorization

    Deep Exemplar-based Video Colorization

    The source code of CVPR 2019 paper "Deep Exemplar-based Colorization"

    ...End-to-end network for exemplar-based video colorization. The main challenge is to achieve temporal consistency while remaining faithful to the reference style. To address this issue, we introduce a recurrent framework that unifies the semantic correspondence and color propagation steps. Both steps allow a provided reference image to guide the colorization of every frame, thus reducing accumulated propagation errors. Video frames are colorized in sequence based on the colorization history, and its coherency is further enforced by the temporal consistency loss. ...
    Downloads: 2 This Week
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  • 12
    GIMP ML

    GIMP ML

    AI for GNU Image Manipulation Program

    ...Additionally, operations on images such as edge detection and color clustering have also been added. GIMP-ML relies on standard Python packages such as numpy, scikit-image, pillow, pytorch, open-cv, scipy. In addition, GIMP-ML also aims to bring the benefits of using deep learning networks used for computer vision tasks to routine image processing workflows.
    Downloads: 10 This Week
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  • 13
    HyperGAN

    HyperGAN

    Composable GAN framework with api and user interface

    A composable GAN built for developers, researchers, and artists. HyperGAN builds generative adversarial networks in PyTorch and makes them easy to train and share. HyperGAN is currently in pre-release and open beta. Everyone will have different goals when using hypergan. HyperGAN is currently beta. We are still searching for a default cross-data-set configuration. Each of the examples supports search. Automated search can help find good configurations. If you are unsure, you can start with...
    Downloads: 0 This Week
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  • 14
    Exposure

    Exposure

    Learning infinite-resolution image processing with GAN and RL

    Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a differentiable photo editing model. ACM Transactions on Graphics (presented at SIGGRAPH 2018) Exposure is originally designed for RAW photos, which assumes 12+ bit color depth and linear "RGB" color space (or whatever we get after demosaicing). jpg and png images typically have only 8-bit color depth (except 16-bit pngs) and the lack of information (dynamic range/activation resolution) may lead to suboptimal results such as posterization. ...
    Downloads: 0 This Week
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