#python #computer_vision #contrastive_learning #contributions_welcome #deep_learning #embeddings #hacktoberfest #machine_learning #pytorch #self_supervised_learning

https://github.com/lightly-ai/lightly

#python #classification #coco #computer_vision #deep_learning #hacktoberfest #image_processing #instance_segmentation #low_code #machine_learning #metrics #object_detection #oriented_bounding_box #pascal_voc #python #pytorch #tensorflow #tracking #video_processing #yolo

Supervision is a powerful tool for building computer vision applications. It allows you to easily load datasets, draw detections on images or videos, and count detections in specific zones. You can use any classification, detection, or segmentation model with it, and it has connectors for popular libraries like Ultralytics and Transformers. Supervision also offers customizable annotators to visualize your data and utilities to manage datasets in various formats. By using Supervision, you can streamline your computer vision projects and make them more reliable and efficient. Additionally, there are extensive tutorials and documentation available to help you get started quickly.

https://github.com/roboflow/supervision

#python #book #chinese #computer_vision #deep_learning #machine_learning #natural_language_processing #notebook #python

This resource, "Dive into Deep Learning," is a free online book that helps you learn deep learning by doing. It provides detailed concepts, background knowledge, and executable code to help you understand the mathematical principles and implement them in practice. The book includes runnable code examples so you can see how to solve problems step-by-step and experiment with different approaches. It also allows for community feedback and continuous updates to keep up with the rapidly evolving field of deep learning. This makes it an excellent resource for anyone looking to become a deep learning practitioner, whether you're a student or an industry professional.

https://github.com/d2l-ai/d2l-zh

#javascript #annotation #annotation_tool #annotations #boundingbox #computer_vision #data_labeling #dataset #datasets #deep_learning #image_annotation #image_classification #image_labeling #image_labelling_tool #label_studio #labeling #labeling_tool #mlops #semantic_segmentation #text_annotation #yolo

Label Studio is a free, open-source tool that helps you label different types of data like images, audio, text, videos, and more. It has a simple and user-friendly interface that makes it easy to prepare or improve your data for machine learning models. You can customize it to fit your needs and export labeled data in various formats. It supports multi-user labeling, multiple projects, and integration with machine learning models for pre-labeling and active learning. You can install it locally using Docker, pip, or other methods, or deploy it in cloud services like Heroku or Google Cloud Platform. This tool streamlines your data labeling process and helps you create more accurate ML models.

https://github.com/HumanSignal/label-studio

#cplusplus #arknights #computer_vision #maa

MAA Assistant Arknights is a powerful tool designed to help players of the game "Arknights" automate daily tasks. It uses image recognition technology to complete tasks such as daily missions, recruiting operators, and managing base facilities. The tool supports multiple platforms including Windows, Linux, and macOS.

Using MAA Assistant Arknights, you can automatically complete daily routines like collecting credits, shopping, and receiving rewards. It also helps in identifying operator lists, tracking materials needed for development, and optimizing base scheduling. The tool integrates with various platforms like Penguin Logistics and Yituliu to upload data and plan strategies.

By using this assistant, you save time and effort by automating repetitive tasks, allowing you to focus on other aspects of the game or your daily life. Additionally, it supports multiple languages and has an active community for support and development contributions.

Overall, MAA Assistant Arknights makes playing Arknights more efficient and enjoyable by handling mundane tasks automatically.

https://github.com/MaaAssistantArknights/MaaAssistantArknights

#python #chinese #clip #computer_vision #contrastive_loss #coreml_models #deep_learning #image_text_retrieval #multi_modal #multi_modal_learning #nlp #pretrained_models #pytorch #transformers #vision_and_language_pre_training #vision_language

This project is about a Chinese version of the CLIP (Contrastive Language-Image Pretraining) model, trained on a large dataset of Chinese text and images. Here’s what you need to know This model helps you quickly perform tasks like calculating text and image features, cross-modal retrieval (finding images based on text or vice versa), and zero-shot image classification (classifying images without any labeled examples).
- **Ease of Use** The model has been tested on various datasets and shows strong performance in zero-shot image classification and cross-modal retrieval tasks.
- **Resources**: The project includes pre-trained models, training and testing codes, and detailed tutorials on how to use the model for different tasks.

Overall, this project makes it easy to work with Chinese text and images using advanced AI techniques, saving you time and effort.

https://github.com/OFA-Sys/Chinese-CLIP

#jupyter_notebook #computer_vision #deep_learning #drug_discovery #forecasting #large_language_models #mxnet #nlp #paddlepaddle #pytorch #recommender_systems #speech_recognition #speech_synthesis #tensorflow #tensorflow2 #translation

This repository provides top-quality deep learning examples that are easy to train and deploy on NVIDIA GPUs. It includes a wide range of models for computer vision, natural language processing, recommender systems, speech to text, and more. These examples are updated monthly and come in Docker containers with the latest NVIDIA software, ensuring the best performance. The models support multiple GPUs and nodes, and some are optimized for Tensor Cores, which can significantly speed up training. This makes it easier for users to achieve high accuracy and performance in their deep learning projects.

https://github.com/NVIDIA/DeepLearningExamples

#cplusplus #android #audio_processing #c_plus_plus #calculator #computer_vision #deep_learning #framework #graph_based #graph_framework #inference #machine_learning #mediapipe #mobile_development #perception #pipeline_framework #stream_processing #video_processing

MediaPipe is a tool that helps you add smart machine learning features to your apps and devices. It works on mobile, web, desktop, and other devices. You can use pre-made solutions for tasks like vision, text, and audio processing, or customize the models to fit your needs. MediaPipe also offers tools like Model Maker and Studio to help you create and test your solutions easily. This makes it easier to delight your customers with innovative features without needing deep machine learning expertise.

https://github.com/google-ai-edge/mediapipe

#python #autogluon #automated_machine_learning #automl #computer_vision #data_science #deep_learning #ensemble_learning #forecasting #gluon #hyperparameter_optimization #machine_learning #natural_language_processing #object_detection #python #pytorch #scikit_learn #structured_data #tabular_data #time_series #transfer_learning

AutoGluon makes machine learning easy and fast. With just a few lines of code, you can train and use high-accuracy models for images, text, time series, and tabular data. This means you can quickly build and deploy powerful machine learning models without needing to write a lot of code. It supports Python 3.8 to 3.11 and works on Linux, MacOS, and Windows, making it convenient for various users. This saves time and effort, allowing you to focus on other parts of your project.

https://github.com/autogluon/autogluon

#other #algorithms #bioinformatics #computational_biology #computational_physics #computer_architecture #computer_science #computer_vision #database_systems #databases #deep_learning #embedded_systems #machine_learning #quantum_computing #reinforcement_learning #robotics #security #systems #web_development

This collection of computer science courses offers a wide range of topics, from introductory programming to advanced specialized fields like machine learning, blockchain, and quantum computing. Here’s a simple summary of the benefits You can learn various aspects of computer science, including programming, algorithms, data structures, computer systems, software engineering, artificial intelligence, machine learning, and more.
- **Diverse Resources** There are courses on specific areas such as blockchain development, quantum computing, computational finance, and robotics, which can help you specialize in your area of interest.
- **Practical Skills** Most of these resources are available online for free, making quality education accessible to everyone.

Overall, this collection is a valuable resource for anyone looking to learn or deepen their knowledge in computer science.

https://github.com/Developer-Y/cs-video-courses

#python #3d_computer_vision #computer_vision #embodied_ai #reinforcement_learning #robot_learning #robot_manipulation #robotics #robotics_simulation #simulation_environment

ManiSkill 3 is a powerful tool for simulating and training robots, especially for tasks that involve manipulating objects. It uses GPU power to collect and simulate data very quickly, up to 30,000 frames per second, which is much faster than other simulators. This makes it great for testing and training robots in various scenarios without needing a lot of time or hardware. It supports different types of robots and tasks, and it's easy to set up and use, even on Google Colab without your own hardware. However, it's still in beta, so some features are not yet available and there might be bugs. Overall, ManiSkill 3 helps users train and test robots much more efficiently.

https://github.com/haosulab/ManiSkill

#rust #ai #computer_vision #llm #machine_learning #ml #multimodal #vision

ScreenPipe is an AI assistant that records your screen and voice 24/7, giving you all the context you need. It's like having a personal recorder that helps you remember everything. You can use it as a desktop app, command line tool, or even integrate it into other applications. The benefit is that you'll never miss important details again, and you can prepare for the future where data is crucial. Plus, it's open-source, so you can customize it to your needs. Downloading ScreenPipe can help you stay organized and prepared in the age of super intelligence.

https://github.com/mediar-ai/screenpipe

#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

#rust #computer_vision #cpp #multimodal #python #robotics #rust #visualization

Rerun is a tool that helps you understand and improve complex processes by logging and visualizing multimodal data like images, 3D points, text, and more. It's useful in areas such as robotics, simulation, and computer vision. You can easily log data using the Rerun SDK in C++, Python, or Rust and visualize it in real-time or save it for later. This helps you debug issues, like why a robot might be malfunctioning, by seeing all the data streams over time. Rerun also allows you to extract clean datasets for training models, making it a powerful tool for development and research. It's free, open-source, and easy to get started with, requiring no account setup.

https://github.com/rerun-io/rerun

#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

#jupyter_notebook #computer_vision #ethical_hacking #face_detection #machine_learning #natural_language_processing #network_analysis #network_programming #network_security #programming_tutorial #python #python_tutorials #python3 #scapy #scapy_tutorials #socket_programming #text_classification #tutorials #web_scraping

This repository offers a wide range of Python tutorials and projects, covering various topics such as ethical hacking, machine learning, web scraping, GUI programming, game development, and more. You can learn how to perform network manipulation, build machine learning models, scrape websites, create GUI applications, develop games, and much more. The tutorials are well-structured and include code examples, making it easy to follow along and implement the projects yourself. This resource is beneficial for both beginners and advanced users looking to expand their Python skills in different areas.

https://github.com/x4nth055/pythoncode-tutorials

#jupyter_notebook #ai #computer_vision #computervision #deep_learning #deep_neural_networks #deeplearning #machine_learning #opencv #opencv_cpp #opencv_library #opencv_python #opencv_tutorial #opencv3

Learning OpenCV and AI can greatly benefit your career by opening up opportunities in fields like autonomous vehicles, healthcare, and robotics. OpenCV University offers comprehensive courses that teach computer vision and deep learning using frameworks like PyTorch. These courses are project-based, providing hands-on experience with real-world applications. By mastering these skills, you can develop innovative solutions and even start your own AI company. The courses are accessible to beginners and offer lifetime access for continuous learning.

https://github.com/spmallick/learnopencv

#jupyter_notebook #computer_vision #deep_learning #inference #machine_learning #openvino

OpenVINO Notebooks are a collection of interactive Jupyter notebooks that help developers learn and experiment with the OpenVINO Toolkit. These notebooks provide an introduction to OpenVINO basics and show how to optimize deep learning inference using the API. They can be run on various platforms, including Windows, Ubuntu, macOS, and cloud services like Azure ML or Google Colab. This makes it easy for users to get started with AI development without needing extensive hardware knowledge, allowing them to focus on building applications efficiently across different devices.

https://github.com/openvinotoolkit/openvino_notebooks

#jupyter_notebook #cnn #colab #colab_notebook #computer_vision #deep_learning #deep_neural_networks #fourier #fourier_convolutions #fourier_transform #gan #generative_adversarial_network #generative_adversarial_networks #high_resolution #image_inpainting #inpainting #inpainting_algorithm #inpainting_methods #pytorch

LaMa is a powerful tool for removing objects from images. It uses special techniques called Fourier Convolutions, which help it understand the whole image at once. This makes it very good at filling in large areas that are missing. LaMa can even work well with high-resolution images, even if it was trained on smaller ones. This means you can use it to fix photos where objects are in the way, making them look natural and complete again.

https://github.com/advimman/lama

#other #artificial_intelligence #artificial_intelligence_projects #awesome #computer_vision #computer_vision_project #data_science #deep_learning #deep_learning_project #machine_learning #machine_learning_projects #nlp #nlp_projects #python

You can access a huge, constantly updated list of over 500 artificial intelligence projects with ready-to-use code covering machine learning, deep learning, computer vision, and natural language processing. This collection includes projects for beginners and advanced users, with links to tutorials, datasets, and real-world applications like chatbots, healthcare, and time series forecasting. Using this resource helps you learn AI by doing practical projects, speeding up your coding skills, and building a strong portfolio for jobs or research. It saves you time searching for quality projects and gives you tested, working code to study and modify.

https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code