#typescript #cancer_imaging_research #dicom #dicom_viewer #hacktoberfest #healthcare_imaging #image_analysis #imaging #imaging_informatics #javascript #medical #medical_image_processing #medical_imaging #nci_itcr #nci_qin #quantitative_imaging #reactjs
The OHIF Medical Imaging Viewer is a powerful tool for viewing medical images. It can load images from various sources and formats, and it supports 2D, 3D, and other advanced viewing options like volume rendering and segmentation. The viewer is highly customizable and extendable, allowing you to add new features as needed. It also supports internationalization, offline use, and user access control. The benefit to you is that it provides a flexible and robust platform for medical image analysis, tailored to your specific needs, with a strong community and extensive support resources.
https://github.com/OHIF/Viewers
The OHIF Medical Imaging Viewer is a powerful tool for viewing medical images. It can load images from various sources and formats, and it supports 2D, 3D, and other advanced viewing options like volume rendering and segmentation. The viewer is highly customizable and extendable, allowing you to add new features as needed. It also supports internationalization, offline use, and user access control. The benefit to you is that it provides a flexible and robust platform for medical image analysis, tailored to your specific needs, with a strong community and extensive support resources.
https://github.com/OHIF/Viewers
GitHub
GitHub - OHIF/Viewers: OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages
OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages - OHIF/Viewers
#python #ade20k #image_classification #imagenet #mask_rcnn #mscoco #object_detection #semantic_segmentation #swin_transformer
The Swin Transformer is a powerful tool for computer vision tasks like image classification, object detection, semantic segmentation, and video recognition. It uses a hierarchical structure with shifted windows to efficiently process images, making it more efficient than other models. Here are the key benefits Swin Transformer achieves state-of-the-art results in various tasks such as COCO object detection, ADE20K semantic segmentation, and ImageNet classification.
- **Efficiency** The model supports multiple tasks including image classification, object detection, instance segmentation, semantic segmentation, and video action recognition.
- **Improved Speed** The model is integrated into popular frameworks like Hugging Face Spaces and PaddleClas, making it easy to use and deploy.
Overall, the Swin Transformer offers high accuracy, efficiency, and versatility, making it a valuable tool for various computer vision applications.
https://github.com/microsoft/Swin-Transformer
The Swin Transformer is a powerful tool for computer vision tasks like image classification, object detection, semantic segmentation, and video recognition. It uses a hierarchical structure with shifted windows to efficiently process images, making it more efficient than other models. Here are the key benefits Swin Transformer achieves state-of-the-art results in various tasks such as COCO object detection, ADE20K semantic segmentation, and ImageNet classification.
- **Efficiency** The model supports multiple tasks including image classification, object detection, instance segmentation, semantic segmentation, and video action recognition.
- **Improved Speed** The model is integrated into popular frameworks like Hugging Face Spaces and PaddleClas, making it easy to use and deploy.
Overall, the Swin Transformer offers high accuracy, efficiency, and versatility, making it a valuable tool for various computer vision applications.
https://github.com/microsoft/Swin-Transformer
GitHub
GitHub - microsoft/Swin-Transformer: This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer…
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows". - microsoft/Swin-Transformer
#cplusplus #ai #api #audio_generation #distributed #gemma #gpt4all #image_generation #kubernetes #llama #llama3 #llm #mamba #mistral #musicgen #p2p #rerank #rwkv #stable_diffusion #text_generation #tts
LocalAI is a free, open-source alternative to OpenAI that you can run on your own computer or server. It allows you to generate text, images, and audio locally without needing a GPU. You can use it with various models and it supports multiple functionalities like text-to-audio, audio-to-text, and image generation. LocalAI is easy to set up using an installer script or Docker, and it has a user-friendly web interface. This tool is beneficial because it saves you money by not requiring cloud services and gives you full control over your data privacy. Plus, it's community-driven, so there are many resources and integrations available to help you get started and customize it to your needs.
https://github.com/mudler/LocalAI
LocalAI is a free, open-source alternative to OpenAI that you can run on your own computer or server. It allows you to generate text, images, and audio locally without needing a GPU. You can use it with various models and it supports multiple functionalities like text-to-audio, audio-to-text, and image generation. LocalAI is easy to set up using an installer script or Docker, and it has a user-friendly web interface. This tool is beneficial because it saves you money by not requiring cloud services and gives you full control over your data privacy. Plus, it's community-driven, so there are many resources and integrations available to help you get started and customize it to your needs.
https://github.com/mudler/LocalAI
GitHub
GitHub - mudler/LocalAI: :robot: The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop…
:robot: The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop-in replacement for OpenAI, running on consumer-grade hardware. No GPU required. Runs gguf,...
#python #artificial_intelligence #attention_mechanism #computer_vision #image_classification #transformers
This text describes a comprehensive implementation of Vision Transformers (ViT) in PyTorch, offering various models and techniques for image classification. Here’s the key information and benefits**
- The repository provides multiple ViT variants, including the original ViT, Simple ViT, NaViT, Deep ViT, CaiT, Token-to-Token ViT, CCT, Cross ViT, PiT, LeViT, CvT, Twins SVT, RegionViT, CrossFormer, ScalableViT, SepViT, MaxViT, NesT, MobileViT, XCiT, and others.
- Each variant introduces different architectural improvements such as efficient attention mechanisms, multi-scale processing, and innovative embedding techniques.
- The implementation includes pre-trained models and supports various tasks like masked image modeling, distillation, and self-supervised learning.
**Benefits** Users can choose from a wide range of ViT models tailored for different needs, such as efficiency, performance, or specific tasks.
- **Performance** Some models, like NaViT and ScalableViT, are designed to be more efficient in terms of computational resources and training time.
- **Ease of Use** The inclusion of various research ideas and techniques allows users to explore new approaches in vision transformer research.
Overall, this repository offers a powerful toolkit for anyone working with vision transformers, providing both practical solutions and cutting-edge research opportunities.
https://github.com/lucidrains/vit-pytorch
This text describes a comprehensive implementation of Vision Transformers (ViT) in PyTorch, offering various models and techniques for image classification. Here’s the key information and benefits**
- The repository provides multiple ViT variants, including the original ViT, Simple ViT, NaViT, Deep ViT, CaiT, Token-to-Token ViT, CCT, Cross ViT, PiT, LeViT, CvT, Twins SVT, RegionViT, CrossFormer, ScalableViT, SepViT, MaxViT, NesT, MobileViT, XCiT, and others.
- Each variant introduces different architectural improvements such as efficient attention mechanisms, multi-scale processing, and innovative embedding techniques.
- The implementation includes pre-trained models and supports various tasks like masked image modeling, distillation, and self-supervised learning.
**Benefits** Users can choose from a wide range of ViT models tailored for different needs, such as efficiency, performance, or specific tasks.
- **Performance** Some models, like NaViT and ScalableViT, are designed to be more efficient in terms of computational resources and training time.
- **Ease of Use** The inclusion of various research ideas and techniques allows users to explore new approaches in vision transformer research.
Overall, this repository offers a powerful toolkit for anyone working with vision transformers, providing both practical solutions and cutting-edge research opportunities.
https://github.com/lucidrains/vit-pytorch
GitHub
GitHub - lucidrains/vit-pytorch: Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with…
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch - lucidrains/vit-pytorch
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#python #annotation #annotation_tool #annotations #boundingbox #computer_vision #computer_vision_annotation #dataset #deep_learning #image_annotation #image_classification #image_labeling #image_labelling_tool #imagenet #labeling #labeling_tool #object_detection #pytorch #semantic_segmentation #tensorflow #video_annotation
CVAT is a powerful tool for annotating videos and images, especially useful for computer vision projects. It helps developers and companies annotate data quickly and efficiently. You can use CVAT online for free or subscribe for more features like unlimited data and integrations with other tools. It also offers a self-hosted option with enterprise support. CVAT supports many annotation formats and has automatic labeling options to speed up your work. It's widely used by many teams worldwide, making it a reliable choice for your data annotation needs.
https://github.com/cvat-ai/cvat
CVAT is a powerful tool for annotating videos and images, especially useful for computer vision projects. It helps developers and companies annotate data quickly and efficiently. You can use CVAT online for free or subscribe for more features like unlimited data and integrations with other tools. It also offers a self-hosted option with enterprise support. CVAT supports many annotation formats and has automatic labeling options to speed up your work. It's widely used by many teams worldwide, making it a reliable choice for your data annotation needs.
https://github.com/cvat-ai/cvat
GitHub
GitHub - cvat-ai/cvat: Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams…
Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. - cvat-ai/cvat
#python #auto_regressive_model #autoregressive_models #diffusion_models #generative_ai #generative_model #gpt #gpt_2 #image_generation #large_language_models #neurips #transformers #vision_transformer
VAR (Visual Autoregressive Modeling) is a new way to generate images that improves upon existing methods. It uses a "next-scale prediction" approach, which means it generates images from coarse to fine details, unlike the traditional method of predicting pixel by pixel. This makes VAR models better than diffusion models for the first time. You can try VAR on a demo website and generate images interactively, which is fun and easy. VAR also follows power-law scaling laws, making it efficient and scalable. The benefit to you is that you can create high-quality images quickly and easily, and even explore technical details through provided scripts and models.
https://github.com/FoundationVision/VAR
VAR (Visual Autoregressive Modeling) is a new way to generate images that improves upon existing methods. It uses a "next-scale prediction" approach, which means it generates images from coarse to fine details, unlike the traditional method of predicting pixel by pixel. This makes VAR models better than diffusion models for the first time. You can try VAR on a demo website and generate images interactively, which is fun and easy. VAR also follows power-law scaling laws, making it efficient and scalable. The benefit to you is that you can create high-quality images quickly and easily, and even explore technical details through provided scripts and models.
https://github.com/FoundationVision/VAR
GitHub
GitHub - FoundationVision/VAR: [NeurIPS 2024 Best Paper Award][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official…
[NeurIPS 2024 Best Paper Award][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Predi...
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#python #3d_creation #3d_generation #aigc #diffusion_models #generative_model #image_to_3d
DreamCraft3D is a method to create highly detailed and realistic 3D objects using a combination of 2D reference images and advanced algorithms. It ensures that the 3D objects look consistent from all angles and have realistic textures. This is achieved by using a special technique called "Bootstrapped Score Distillation" which improves both the shape and texture of the 3D object in a way that reinforces each other. The benefit to the user is that they can generate very realistic 3D models quickly and accurately, which can be useful for various applications such as video games, movies, and architectural design.
https://github.com/deepseek-ai/DreamCraft3D
DreamCraft3D is a method to create highly detailed and realistic 3D objects using a combination of 2D reference images and advanced algorithms. It ensures that the 3D objects look consistent from all angles and have realistic textures. This is achieved by using a special technique called "Bootstrapped Score Distillation" which improves both the shape and texture of the 3D object in a way that reinforces each other. The benefit to the user is that they can generate very realistic 3D models quickly and accurately, which can be useful for various applications such as video games, movies, and architectural design.
https://github.com/deepseek-ai/DreamCraft3D
GitHub
GitHub - deepseek-ai/DreamCraft3D: [ICLR 2024] Official implementation of DreamCraft3D: Hierarchical 3D Generation with Bootstrapped…
[ICLR 2024] Official implementation of DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior - deepseek-ai/DreamCraft3D
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#python #image_processing #ocr #pdf #python #tesseract
OCRmyPDF is a tool that makes scanned PDF files searchable and editable. It adds a text layer to the PDF, so you can search for words or copy and paste text from the document. It supports many languages, fixes misrotated or crooked pages, and optimizes the file size. The tool works on various operating systems like Linux, Windows, and macOS, and it uses multiple CPU cores to speed up the process. This makes it easier to work with scanned documents and keeps your files organized and searchable.
https://github.com/ocrmypdf/OCRmyPDF
OCRmyPDF is a tool that makes scanned PDF files searchable and editable. It adds a text layer to the PDF, so you can search for words or copy and paste text from the document. It supports many languages, fixes misrotated or crooked pages, and optimizes the file size. The tool works on various operating systems like Linux, Windows, and macOS, and it uses multiple CPU cores to speed up the process. This makes it easier to work with scanned documents and keeps your files organized and searchable.
https://github.com/ocrmypdf/OCRmyPDF
GitHub
GitHub - ocrmypdf/OCRmyPDF: OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched
OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched - ocrmypdf/OCRmyPDF
#kotlin #aes_256 #android #background_removal #clean_architecture #crop #djvu #edit_photo #exif #f_droid #filter_image #image_manipulation #jetpack_compose #jxl #kotlin #material_you #ocr_recognition #pdf #psd #qrcode_scanner #watermark
Image Toolbox is a powerful and versatile image editing tool that lets you do many things with your photos. You can crop, apply over 230 different filters, edit EXIF data, remove backgrounds, and even convert images to PDFs. It also allows you to add stickers and text, extract text from images in over 120 languages, and encrypt files with AES-256 encryption. You can resize images using various scaling algorithms, convert between multiple image formats, and create collages. The app also supports GIF, WEBP, APNG, and JXL conversions, document scanning, QR code scanning and creation, and more. It has a simple interface but offers many advanced features, making it useful for both photographers and developers.
https://github.com/T8RIN/ImageToolbox
Image Toolbox is a powerful and versatile image editing tool that lets you do many things with your photos. You can crop, apply over 230 different filters, edit EXIF data, remove backgrounds, and even convert images to PDFs. It also allows you to add stickers and text, extract text from images in over 120 languages, and encrypt files with AES-256 encryption. You can resize images using various scaling algorithms, convert between multiple image formats, and create collages. The app also supports GIF, WEBP, APNG, and JXL conversions, document scanning, QR code scanning and creation, and more. It has a simple interface but offers many advanced features, making it useful for both photographers and developers.
https://github.com/T8RIN/ImageToolbox
GitHub
GitHub - T8RIN/ImageToolbox: 🖼️ Image Toolbox is a powerful app for advanced image manipulation. It offers dozens of features,…
🖼️ Image Toolbox is a powerful app for advanced image manipulation. It offers dozens of features, from basic tools like crop and draw to filters, OCR, and a wide range of image processing options -...
#go #automation #c #go #golang #hook #image #mouse #opencv #robot #robotgo #rpa #window
Robotgo is a tool that helps automate tasks on your computer using the Go programming language. It can control the mouse and keyboard, capture screenshots, and work with windows. This means you can use it to automatically do things like scrolling, clicking, or typing text. Robotgo works on Windows, Mac, and Linux systems, making it very versatile. Using Robotgo can save time by automating repetitive tasks, allowing you to focus on more important things.
https://github.com/go-vgo/robotgo
Robotgo is a tool that helps automate tasks on your computer using the Go programming language. It can control the mouse and keyboard, capture screenshots, and work with windows. This means you can use it to automatically do things like scrolling, clicking, or typing text. Robotgo works on Windows, Mac, and Linux systems, making it very versatile. Using Robotgo can save time by automating repetitive tasks, allowing you to focus on more important things.
https://github.com/go-vgo/robotgo
GitHub
GitHub - go-vgo/robotgo: RobotGo, Go Native cross-platform RPA, GUI automation, Auto test and Computer use @vcaesar
RobotGo, Go Native cross-platform RPA, GUI automation, Auto test and Computer use @vcaesar - go-vgo/robotgo