Open-source alternatives to Quickdraw Dataset

This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy of machine learning models. It provides a standardized collection of labeled fashion product images and training data formatted to be compatible with the MNIST dataset structure. The dataset consists of fixed-dimension grayscale images and label-based category mappings, stored in a binary format. It includes pre-split training and testing sets and a static distribution to ensure consistent cross-model benchmarking. The repository supports image classification benchmarking and

This project is a computer vision dataset and image annotation repository designed for training and evaluating machine learning models. It provides a large collection of labeled images, serving as an object detection benchmark and a source of pixel-level segmentation data. The repository distinguishes itself as a multimodal visual dataset by pairing images with synchronized voice, text, and mouse traces to support narrative understanding. It further enables the analysis of model fairness through the inclusion of demographic attributes and exhaustive annotations. The dataset covers a broad ra

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

XcodeBuildMCP is a Model Context Protocol server and development tool bridge that provides AI agents with the ability to control xcodebuild, manage simulators, and automate the compilation and execution of Apple platform applications. It functions as a persistent daemon that proxies native IDE build and debug capabilities to external clients and agents. The project distinguishes itself by using the Model Context Protocol to expose build and device management tools through a standardized interface. It implements specialized skill priming and instruction configuration to ensure AI agents can in

xcodebuildmcp is a Model Context Protocol server that exposes Xcode build, test, and device management tools for AI coding agents to automate iOS and macOS development workflows. It operates as a background daemon per workspace, communicating tool requests and responses over standard input/output using JSON-RPC messages, and streams progress and results as newline-delimited JSON objects for machine parsing. The project provides an interactive setup wizard and file-based client configuration to install skill files into predefined directories for supported AI coding clients. It manages the full

Agent-S is a multimodal AI agent and LLM desktop automation framework designed to control operating systems through graphical user interface interactions. It functions as a computer use interface, utilizing vision-language grounding to translate natural language goals into precise screen coordinates and system actions. The project differentiates itself by combining structured accessibility tree inspection with vision-based element localization. It manages cross-application workflows by mapping conceptual descriptions to physical pixels and simulating low-level keyboard and mouse events to mov

This project is an object detection evaluation library and benchmarking tool designed to calculate precision, recall, and average precision for computer vision models. It provides a suite of utilities for parsing bounding box coordinates from text files and calculating spatial overlap to determine detection accuracy. The toolkit features a command line interface for comparing ground truth files against model predictions. It includes a precision-recall curve generator to visualize the relationship between precision and recall across different confidence thresholds and an intersection over unio

Danfo.js is a data analysis and preprocessing library for JavaScript that provides high-performance labeled data structures. It implements data frames and series to enable complex data analysis, statistical computing, and the manipulation of structured tabular data. The project serves as a machine learning preprocessing library, offering utilities for categorical label encoding, one-hot encoding, and numeric feature scaling and standardization. It specifically facilitates the conversion of labeled data structures into tensors for model training and evaluation. The library covers a broad set

signature_pad is a JavaScript library and drawing tool for capturing handwritten digital signatures on an HTML5 canvas. It provides a handwriting capture interface that transforms raw mouse or touch inputs into smooth, professional-looking lines. The project uses Bézier curve interpolation to smooth jagged input points into fluid strokes. It supports the configuration of drawing aesthetics, including pen color, background color, and line width, and includes a history management system for undoing and restoring drawing actions. The library handles the serialization of signature data into poin

Data-Juicer is an open-source framework for cleaning, filtering, deduplicating, and transforming multimodal datasets to prepare them for training large language and vision models. It functions as a distributed data pipeline engine that runs processing jobs across Ray clusters, handling billions of samples with automatic operator fusion and adaptive parallelism. The framework provides a library of operators that leverage large language models for semantic extraction, filtering, and data synthesis within processing pipelines. The project distinguishes itself through a YAML-based data recipe sys

Claude Quickstarts is a development framework and collection of reference implementations designed for building autonomous agents. It provides the foundational patterns necessary to orchestrate multi-agent workflows, enabling models to perform complex, multi-step tasks across software engineering, customer support, and computer-use domains. The platform distinguishes itself through specialized capabilities for desktop and browser automation, allowing agents to interact with graphical interfaces by capturing visual context and executing precise mouse and keyboard inputs. It includes robust inf

This project provides a translated version of the scikit-learn machine learning library guides and API references for Chinese speakers. It serves as a localized knowledge base and technical reference for implementing predictive data analysis and statistical modeling using a Python-based toolkit. The resource covers the implementation of supervised learning, including classification and regression tasks, and unsupervised learning workflows for pattern discovery and anomaly detection. It also provides guidance on data science education, specifically focusing on the use of scikit-learn for machi

This repository is a curated study resource of interview questions and answers for data science roles. It covers the core domains of machine learning, statistics, Python programming, SQL databases, deep learning, and algorithmic problem solving. The content is organized as static Markdown files with a structured question-and-answer format, making it easy to read and navigate without any server-side processing. The material distinguishes itself by pairing each question with a detailed explanation and often a code example, covering both conceptual knowledge and practical application. Topics ran

The Google Workspace CLI is a command-line interface and Google API client designed to automate tasks across Google Workspace services. It functions as a cloud productivity automator that uses the Google Discovery Service to dynamically generate command structures and parameter requirements at runtime. The project distinguishes itself by providing a specialized AI agent toolset, exposing a server over standard input and output to provide structured tool definitions and skills for AI clients. It includes security layers for AI content sanitization to protect against prompt injection and utiliz

missingno is a Python library for the visualization and analysis of missing data patterns. It provides a set of tools to profile dataset completeness, map data gaps, and quantify the volume of null values across variables. The library differentiates itself through a nullity correlation analyzer and a hierarchical data clustering tool. These components allow for the detection of systemic dependencies and trends by measuring how the absence of one variable relates to the absence of another. The toolset covers broader data quality auditing and exploratory analysis capabilities. It includes feat

This project is a REST-to-gRPC API gateway and JSON reverse proxy that translates RESTful HTTP requests into gRPC service calls. It functions as a protocol buffer proxy generator, providing the tools necessary to bridge JSON-based HTTP traffic with backend gRPC servers. The system distinguishes itself by automating the creation of reverse-proxy servers and stubs through protobuf-driven code generation. It also includes a dedicated OpenAPI specification generator that produces OpenAPI v2 and v3 documents from gRPC service definitions and annotations. The project covers a broad range of integr

Gron is a command line utility that transforms nested JSON data into a flat list of path-value assignments. This process converts hierarchical structures into line-based statements, mapping every leaf value to its absolute path to make the data compatible with standard text-processing tools. The tool allows for the bidirectional transformation of data, enabling the reconstruction of original nested JSON objects from flattened path assignments. It can ingest JSON from local files, standard input, or remote URLs, with the ability to route network traffic through proxy servers. The utility supp

msgspec is a high-performance data modeling, serialization, and schema validation toolkit for Python. It serves as a type-safe serialization framework that integrates schema enforcement and data parsing into a single pass, functioning as both a data serialization library and a schema validation system based on standard Python type annotations. The project distinguishes itself through high-performance structural primitives, including compilation-based routine generation and zero-copy buffer parsing. It optimizes memory usage via garbage collection-aware layouts and reduces processing overhead

LivePortrait is a computer vision framework designed for portrait animation and generative video synthesis. It functions as a deep learning system that transfers facial expressions and head movements from a driving video source onto a static image or an existing portrait video, effectively decoupling the subject's identity from the dynamic motion patterns. The framework utilizes keypoint-based motion retargeting and implicit 3D latent representations to map movements across different subjects, including both human and animal portraits. By employing canonical motion normalization and feature-s

Pokédex of Pokémon GO in JSON. (1st generation)

A simple data project for Steam data.

README

Gaming video streaming applications such as Twitch.tv have gained much attention in the recent years and are currently responsible for a significant share of video streaming over the internet. Unlike traditional Video on Demand (VoD) streaming services, gaming videos are streamed live and hence…

We release the largest StarCraft: Brood War replay dataset yet, with 65646 games. The full dataset after compression is 365 GB, 1535 million frames, and 496 million player actions. The entire frame data was dumped out at 8 frames per second. We made a big effort to ensure this dataset is clean…

A dataset of 2D imagery, 3D point cloud data, and 3D vehicle bounding box labels all generated using the Grand Theft Auto 5 game engine. The dataset contains image and depth map data captured at 1680x1050 resolution and oriented 3D bounding box labels of all vehicles. It is 55GB in total.

This project is a PostgreSQL client library and SQL query builder for JavaScript and TypeScript. It provides a low-level database driver and connection manager to handle database sessions, along with a logical replication client for monitoring real-time changes. The library distinguishes itself with a high-performance bulk data streamer that utilizes the database copy command for importing and exporting large datasets. It also implements a logical replication protocol to facilitate real-time database synchronization through change subscriptions and channel-based notifications. The toolset co