Popular GitHub Repositories for Uci Smartphone Dataset

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

pix2tex: Using a ViT to convert images of equations into LaTeX code.

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.

Open source annotation tool for machine learning practitioners.

A powerful tool for creating fine-tuning datasets for LLM

大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP

Techniques for deep learning with satellite & aerial imagery

BISHENG is an open LLM devops platform for next generation Enterprise AI applications. Powerful and comprehensive features include: GenAI workflow, RAG, Agent, Unified model management, Evaluation, SFT, Dataset Management, Enterprise-level System Management, Observability and more.

Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

A Collection of Variational Autoencoders (VAE) in PyTorch.

AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps.

Awesome Pretrained Chinese NLP Models，高质量中文预训练模型&大模型&多模态模型&大语言模型集合

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

Data processing for and with foundation models! ? ? ? ?? ??? ? ?

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"

The easiest tool for fine-tuning LLM models, synthetic data generation, and collaborating on datasets.

? 目前最大的工业缺陷检测数据库及论文集 Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance.

Models, data loaders and abstractions for language processing, powered by PyTorch

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier