#datasets

Explore open-source code and datasets offered by Google Research, licensed under CC BY 4.0 and Apache 2.0. These resources are valuable for developers and researchers seeking innovative solutions. Access specific subdirectories with GitHub, enabling easy downloads and contributions via shallow clone and pull requests. This repository is not an official Google product.

This compilation offers resources crucial for enhancing images in low light conditions, beneficial for fields such as night surveillance and automated driving. It includes a well-curated selection of datasets and enhancement methods, such as learning-based and Retinex-based approaches, alongside diverse metrics. Recently updated with ICCV2023 papers, it serves as a vital resource for researchers and developers to elevate the quality of low light imagery and video. Contributions and insights are welcomed through the issue tracker or pull requests, promoting a collaborative space for progress in this domain.

ConvoKit provides a unified interface for analyzing conversational data, compatible with scikit-learn. It supports the study of social phenomena through features such as linguistic coordination and politeness strategies, along with hypergraph conversation representation. The toolkit includes neural models for forecasting conversational outcomes and addresses linguistic diversity. Datasets from platforms like Wikipedia and Reddit are readily available, making it a vital resource for analyzing conversational dynamics.

Provides resources for building text-to-SQL systems, including annotated sentences and SQL queries. Incorporates previous and new datasets, supported by IBM and multiple contributors, important for researchers in computational linguistics.

Torchvision offers a robust set of tools for computer vision, including datasets, model architectures, and image transformations. It supports multiple image backends like torch tensors and PIL images and provides video processing options via pyav and video_reader. Additionally, it includes C++ compatible models with a note on version stability. Torchvision focuses on efficiency and ease of use, integrating seamlessly with the PyTorch ecosystem.

This repository provides a complete suite of visual assets, pre-trained models, and curated datasets that integrate smoothly with the Ultralytics YOLO ecosystem. It offers essential tools for object detection, image classification, among others, suitable for both personal and commercial applications. Users can easily download pre-trained models to perform inference with minimal effort. Additionally, it features a wide range of visual assets and datasets to facilitate diverse machine learning projects. With comprehensive documentation and varied licensing options, the repository is designed to support both hobbyists and professionals in advancing their computer vision capabilities.

Explore a community-driven, lightweight library designed for efficient data loading and preprocessing in machine learning applications. It offers one-line data loaders with robust preprocessing capabilities for formats such as CSV, JSON, and images. Experience smart caching, memory-mapping, and seamless integration with frameworks like NumPy, Pandas, PyTorch, and TensorFlow. Benefit from built-in support for audio and image data, along with streaming for efficient large dataset access. An ideal tool for researchers needing a fast, flexible solution with efficient disk usage.

Explore an extensive collection of open-source datasets, models, and tools dedicated to instruction tuning in various NLP tasks. This project offers modified datasets from traditional NLP tasks and those generated by large language models (LLMs), providing multilingual resources for global accessibility. It includes translation tools and processes to improve translation quality in low-resource languages. Additionally, it provides a curated list of academic papers and repositories for exploring instruction-based learning, in-context learning, reasoning, and frameworks, making it a valuable resource for researchers and developers in AI enhancement.

TorchText, an essential NLP library in PyTorch, releases its final version 0.18 in April 2024. The library includes components like datasets, basic NLP models, and pre-trained models. It supports tasks such as language modeling, translation, and classification using datasets and models like RoBERTa. Installable via Anaconda or pip, it remains a valuable NLP resource despite ceasing development.

Delve into extensive archives of academic papers on RGBD semantic segmentation. With regular updates, this repository offers insights on datasets, performance metrics, and benchmark results, supporting focused research. Noteworthy updates highlight integration with key datasets such as NYUDv2, SUN RGB-D, and Cityscapes, enriched by performance assessments using metrics like Pixel Accuracy and mIoU. Uncover the latest methodologies propelling advancements in image segmentation.

Discover LOFT, a benchmark to assess long-context language models in retrieval, reasoning, and additional tasks. With 35 datasets across different modalities, the benchmark evaluates capabilities in retrieval, RAG, SQL, and multi-hop reasoning. Resources include datasets, installation instructions, and evaluation scripts available from a central repository. Gain insights into each dataset, recognize task types, and use scripts for inference and assessment with VertexAI's gemini-1.5-flash-002 model. Understand how these models advance retrieval and reasoning approaches.

Unified-IO 2 offers advanced solutions in multimodal AI by integrating vision, language, audio, and action into one versatile toolset. It includes demo, training, and inference capabilities. Recent updates feature Pytorch code for improved audio processing and VIT-VQGAN integration, supporting complex datasets with robust pre-processing. Designed for both TPU and GPU use, it facilitates efficient training and evaluation with JAX. With T5X architecture, it provides clear data visualization and effective model optimization for specific tasks. Unified-IO 2 stands at the forefront of autoregressive model research, contributing significantly to AI advancement.

Discover an innovative makeup transfer framework excelling in both color and pattern applications. The CPM model integrates an enhanced color branch and an original pattern branch, and offers four new datasets for thorough training and evaluation. Access pre-trained models, follow installation guidelines, and run the framework as detailed in usage instructions. Explore detailed qualitative comparison results online.

FedScale is an open-source platform for federated learning, featuring scalable deployment and extensive evaluation tools for diverse environments. It supports FL experiments with advanced APIs and diverse datasets, including tasks such as image classification and language modeling. FedScale offers scalable and extensible solutions for realistic FL training, proving itself as a vital resource for research and development in federated learning.

ImageInWords provides hyper-detailed image descriptions through its comprehensive dataset collection, available on its webpage and multiple platforms, including Huggingface. These datasets serve as a valuable resource for computer vision research and applications. The project welcomes collaborations and feedback under the CC-BY-4.0 license for easy access and broad usability.

Discover an extensive compilation of resources dedicated to image composition with a focus on realistic object insertion. This list includes scholarly papers, datasets, and a toolbox to tackle inconsistencies in appearance, geometry, and semantics between different image layers. Features include an online demonstration and the 'libcom' library, providing over ten functionalities such as image blending, harmonization, and shadow creation. These tools support advancements in fields like comics, animation, and augmented reality without promotional language.

Explore the Lightning Library for a curated set of PyTorch Lightning tutorials that prioritize efficiency and reproducibility. Utilizing rich script formats, this repository ensures easy collaboration and environment consistency by supporting edits through Python scripts and conversion tools like jupytext. It offers a structured approach to contribution, dataset management, and valuable development insights, making it perfect for those looking to seamlessly create and manage tutorials in PyTorch Lightning.

A detailed repository of medical NLP resources including evaluations, competitions, datasets, papers, and pre-trained models, maintained by third-party contributors. It features Chinese and English benchmarks like CMB, CMExam, and PromptCBLUE, highlights ongoing and past events such as BioNLP Workshop and MedVidQA, and catalogs diverse datasets like Huatuo-26M and MedMentions. The repository also provides access to open-source models like BioBERT and BlueBERT, and large language models including ApolloMoE, catering to researchers in the medical NLP sphere.

rPPG-Toolbox is an efficient open-source tool for remote photoplethysmography, facilitating quick algorithm development for camera-based physiological monitoring. Supporting leading neural and unsupervised methods, it suits diverse developmental requirements. The toolbox features a variety of supervised and traditional algorithms and integrates with seven datasets for extensive physiological research. It provides a simplified setup and uses pre-trained models to enhance data visualization. Key aspects include algorithm benchmarks and detailed configurations for training and testing, making it a valuable resource for researchers and developers in the physiological signal domain.

PyKEEN offers a robust solution for training and evaluating knowledge graph embedding models, featuring multi-modal information support. Its intuitive pipeline function accommodates a wide range of datasets and modeling approaches, beneficial for knowledge graph researchers and practitioners. Integration with Optuna and PyTorch Lightning enhances its extensibility and performance. Comprehensive documentation and tutorials guide users in exploring custom datasets, understanding evaluation metrics, and generating novel link predictions for knowledge graph projects.

This repository offers a vast collection of instruction tuning and learning resources, including papers and datasets, curated by PennState and OhioState experts. Focused on advancing instruction-based learning, it supports the academic community with surveys, corpora, and a collaborative environment, enhancing AI task efficiency. Explore the latest updates and contribute to improving instruction methodologies.

FlowMap utilizes gradient descent for efficient camera pose and depth estimation, demonstrating versatility across datasets like Real Estate 10k and Tanks & Temples. It includes tools for view synthesis via modified 3D Gaussian Splatting, and offers a framework adaptable for photogrammetry purposes. With a Linux-friendly setup, pretrained options, and customizable Hydra configurations, it’s suitable for researchers and developers. Endorsed by leading institutions, FlowMap is both scientifically and practically significant.