Popular GitHub Repositories for Sequence Labeling

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

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 libraries and APIs to build custom preprocessing pipelines for labeling, training, or production machine learning pipelines.

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Open source annotation tool for machine learning practitioners.

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

This repository offers a comprehensive collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like YOLO11, RT-DETR, SAM 2, Florence-2, PaliGemma 2, and Qwen2.5VL.

Effortless data labeling with AI support from Segment Anything and other awesome models.

A system for quickly generating training data with weak supervision

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans

Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets

Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.

A collection of sample apps to demonstrate how to use Google's ML Kit APIs on Android and iOS

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

LightSeq: A High Performance Library for Sequence Processing and Generation

Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything (SAM+SAM2), MobileSAM!!

Database system for AI-powered apps

Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.