Popular GitHub Repositories for Oxford Flower 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.

[CVPR2023] Blur Interpolation Transformer for Real-World Motion from Blur

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

Oxford Deep NLP 2017 course

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.

A powerful tool for creating fine-tuning datasets for LLM

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.

Open source annotation tool for machine learning practitioners.

Techniques for deep learning with satellite & aerial imagery

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

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

A Collection of Variational Autoencoders (VAE) in PyTorch.

Flower: A Friendly Federated AI Framework

CSGHub is a brand-new open-source platform for managing LLMs, developed by the OpenCSG team. It offers both open-source and on-premise/SaaS solutions, with features comparable to Hugging Face. Gain full control over the lifecycle of LLMs, datasets, and agents, with Python SDK compatibility with Hugging Face. Join us! ??

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

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

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

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

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