OpenCV Zoo is a curated collection of pre-trained computer vision models optimized for OpenCV's DNN module, accompanied by inference demo scripts (Python and C++), benchmarking harness, evaluation tools, and quantization utilities. The repository serves as a practical resource for deploying models on a wide range of hardware (x86, ARM, RISC-V, NPUs) by providing ready-to-run code for tasks such as face detection/recognition, object detection, segmentation, text detection/recognition, pose estimation, and more. While primarily an application-oriented model zoo, it also includes research-grade evaluation metrics (e.g., LFW, WIDERFace) and quantization workflows, giving it a hybrid character that bridges model deployment and performance characterization. The benchmarking framework and quantization tools are the most architecturally coherent parts; model-specific demos are utilitarian but tightly coupled to their respective ONNX graphs.
This repository is a lightweight, reference implementation of several standard LLM benchmarks (MMLU, GPQA, MATH, HumanEval, DROP, MGSM, SimpleQA, BrowseComp, HealthBench) developed by OpenAI. It provides a unified evaluation framework based on an `Eval` base class and `SamplerBase` that supports OpenAI Chat Completions API, Anthropic Claude API, OpenAI Responses API, and reasoning models (o1, o3, o4-mini). The main entry point is `simple_evals.py` which accepts command-line arguments to select models and evals, runs them, and outputs HTML reports and JSON metrics. The code is cleanly factored into per-benchmark modules, a shared utility module (`common.py`), and sampler adapters. The framework is designed for reproducibility of OpenAI's published benchmark numbers, with all data loaded from public Azure blob storage URLs. The repository is deprecated as of July 2025 and will not receive updates for new models or benchmarks, though it will continue to host reference implementations for HealthBench, BrowseComp, and SimpleQA.
This repository is a curated collection of machine unlearning research, combining a comprehensive survey paper (in the form of a Kaggle notebook) with executable implementations of several unlearning algorithms across different domains. It includes code for graph neural network unlearning (GraphProjector), federated learning unlearning (FedEraser), and approximate nearest neighbor search with unlearning (UnfoldedSelf-ReconstructionHashing, a fork of ann-benchmarks). The repo serves both as a literature index (taxonomy, evaluation metrics, datasets) and a benchmark hub for reproducing and comparing unlearning methods. While the survey is actively maintained, the algorithm codebases are research prototypes with varying levels of polish and portability.
This repository is a comprehensive, cross-implementation benchmark suite for R7RS Scheme. It provides a standardized harness that compiles and runs approximately 50 benchmarks (covering numerical, symbolic, I/O, GC, and meta-circular workloads) across more than 30 Scheme implementations, capturing execution times and correctness results. The suite exposes portability gaps, performance differences, and implementation bugs through its per-implementation prelude/postlude system, which adapts the common benchmark code to each Scheme's dialect. Results are output as CSV files and aggregated into summary tables and HTML charts (via graph-gmean.scm and graph.scm). While the benchmarks are well-structured and the harness is robust, many implementations are broken or incomplete, preludes are fragile workarounds, and the codebase shows age-related drift (e.g., deprecated APIs, hardcoded paths, and inputs too small for modern hardware).
The Web Tooling Benchmark is a performance measurement suite designed to evaluate JavaScript-related workloads in common web developer tools. It executes benchmarks for tools like Babel, TypeScript, UglifyJS, and others, measuring their throughput in runs per second. The architecture consists of a Node.js CLI interface and a browser-compatible bundle, both driven by a benchmark suite that runs each tool's core functionality on standardized third-party code samples. Key entry points are src/cli.js (for Node.js) and src/bootstrap.js (for browser). The suite reads input files from the third_party directory, processes them via each tool's API, and aggregates results using geometric mean. External dependencies include the benchmarking framework, various JavaScript tools (acorn, babel, typescript, etc.), and utilities for file system virtualization and output formatting.
CameraBench is a benchmark and research project for evaluating camera motion understanding in videos, comparing classical Structure-from-Motion (SfM) methods and modern Vision-Language Models (VLMs). The repository serves as a landing page with download scripts for the test dataset (videos and labels from Hugging Face), a leaderboard showing model performance, and links to fine-tuned Qwen2.5-VL models. The actual evaluation and training code resides in a separate repository (t2v_metrics). The repo lacks a self-contained pipeline, dependency management, or data processing scripts; it is essentially a static site with download helpers. A notebook template for a ReLU activation function exercise is included but is unrelated to the core project. The benchmark is novel and published at NeurIPS 2025, but the codebase itself is minimal and not reusable beyond data retrieval.
This repository is a comparative benchmark of web-to-desktop application frameworks (Electron, Tauri, NW.js, Wails, NodeGui, Neutralino, Flutter, .NET MAUI). It provides automated benchmarking infrastructure that builds empty apps for each framework, measures build size, build time, memory usage, and startup duration across multiple operating systems, and presents the results in formatted README tables and Mermaid graphs. The repository also tracks GitHub stars and forks over time. The codebase is primarily a data collection and presentation project, with the runner scripts orchestrating the benchmarks and generating the comparison documents. The benchmark apps themselves are minimal template applications. The project is actively maintained with recent commits and automated CI workflows, but the runner code contains several technical issues (undefined functions, deprecated patterns) indicating high technical drift. The data is bundled in the repository, making results immediately accessible without running benchmarks.
This repository is a literature survey on recent advances in large language model benchmarks against data contamination, specifically the transition from static to dynamic evaluation. It contains a single README.md file that lists and categorizes numerous research papers, benchmarks, and methods. There is no executable code, data pipelines, notebooks, or any software artifacts. The repository serves as a curated reference list and taxonomy for researchers, but offers no reusable code or data assets. It is not a functional application or a data research project with ETL or model training components.