Stars
Refine high-quality datasets and visual AI models
ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library.
Top conferences & Journals focused on Facial expression recognition (FER)/ Facial action unit (FAU)
Pseudo-labeling for tabular data
ICT's Vision and Graphics Lab's morphable face model and toolkit
Real-time Python demos of google mediapipe
Multimodal model for text and tabular data with HuggingFace transformers as building block for text data
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
(ෆ`꒳´ෆ) A Survey on Text-to-Image Generation/Synthesis.
AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
Source code for "Transforming Question Answering Datasets Into Natural Language Inference Datasets"
A curated list of Visual Question Answering(VQA)(Image/Video Question Answering),Visual Question Generation ,Visual Dialog ,Visual Commonsense Reasoning and related area.
A comprehensive reference for all topics related to Natural Language Processing
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
Library for Knowledge Intensive Language Tasks
The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.
A Unified Library for Parameter-Efficient and Modular Transfer Learning
MATLAB Code for Visual Localization using Particle Filter
PyTorch code for EMNLP 2019 paper "LXMERT: Learning Cross-Modality Encoder Representations from Transformers".
A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)
The "Python Machine Learning (1st edition)" book code repository and info resource
This repository supports Data Science workshop.
🎓 Введение в машинное обучение. Курс от ВШЭ и ШАД на coursera.org