Popular GitHub Repositories for Reducing Labeling Costs

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.

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.

A system for quickly generating training data with weak supervision

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

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

OpenBot leverages smartphones as brains for low-cost robots. We have designed a small electric vehicle that costs about $50 and serves as a robot body. Our software stack for Android smartphones supports advanced robotics workloads such as person following and real-time autonomous navigation.

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.

Images to inference with no labeling (use foundation models to train supervised models).

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

The AI Datastore for Schemas, BLOBs, and Predictions. Use with your apps or integrate built-in Human Supervision, Data Workflow, and UI Catalog to get the most value out of your AI Data.

GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2