Search Results for "benchmark" - Page 6
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Showing 177 open source projects for "benchmark"
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StudioGAN
StudioGAN is a Pytorch library providing implementations of networks
...StudioGAN aims to offer an identical playground for modern GANs so that machine learning researchers can readily compare and analyze a new idea. Moreover, StudioGAN provides an unprecedented-scale benchmark for generative models. The benchmark includes results from GANs (BigGAN-Deep, StyleGAN-XL), auto-regressive models (MaskGIT, RQ-Transformer), and Diffusion models (LSGM++, CLD-SGM, ADM-G-U). StudioGAN is a self-contained library that provides 7 GAN architectures, 9 conditioning methods, 4 adversarial losses, 13 regularization modules, 6 augmentation modules, 8 evaluation metrics, and 5 evaluation backbones. ... -
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Fashion-MNIST
A MNIST-like fashion product database
...Each image has a resolution of 28 by 28 pixels and belongs to one of ten clothing classes, making it suitable for evaluating classification models. Because the dataset represents real-world objects rather than handwritten digits, it offers a more challenging benchmark for testing machine learning algorithms. -
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RLCard
Reinforcement Learning / AI Bots in Card (Poker) Games
RLCard is a toolkit for reinforcement learning research on card games. It includes several popular card games and focuses on learning algorithms for imperfect information games like poker and blackjack. -
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CodeSearchNet
Datasets, tools, and benchmarks for representation learning of code
CodeSearchNet is a large-scale dataset and research benchmark designed to advance the development of systems that retrieve source code using natural language queries. The project was created through collaboration between GitHub and Microsoft Research and aims to support research on semantic code search and program understanding. The dataset contains millions of pairs of source code functions and corresponding documentation comments extracted from open-source repositories. -
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Procgen
Procedurally-Generated Game-Like Gym-Environments
Procgen (short for Procedural Generation Benchmark) is a suite of 16 procedurally generated, game-like reinforcement learning environments designed to evaluate generalization and sample efficiency in RL agents. Unlike fixed, deterministic environments, Procgen generates new levels (layouts, obstacles, visual variation) each episode, making it impossible for an agent to simply memorize trajectories. -
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Following Instructions with Feedback
Training Language Models to Follow Instructions with Human Feedback
...The repository is not a full implementation of the entire RLHF pipeline, but rather an archival hub supporting the published research—providing transparency around evaluation and human labeling standards. It includes directories such as automatic-eval-samples (samples of model outputs on benchmark tasks) and a model-card.md that describes the InstructGPT models’ intended behavior, limitations, and biases. -
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GiantMIDI-Piano
Classical piano MIDI dataset
GiantMIDI-Piano is a large-scale symbolic classical piano music dataset built by applying the piano_transcription system on a vast collection of piano performance recordings. The dataset contains thousands of piano works, spanning a large number of composers and styles, with each piece transcribed into high-precision MIDI files capturing note events, pedal usage, velocities, etc. It provides a resource for music information retrieval (MIR), symbolic music modeling, composer classification,... -
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Grade School Math
8.5K high quality grade school math problems
...The problems are written by human authors (not automatically generated) to ensure linguistic variety and realism. The repository maintains strict formatting (e.g. JSONL) for problem + answer pairs, and is used broadly in research to benchmark model performance under “word problem” settings. Issues are tracked (people report incorrect problems, ambiguous statements), and contributions are possible for cleaning or expanding the set. -
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Awesome Explainable Graph Reasoning
A collection of research papers and software related to explainability
...In the area of graph data, graph neural networks (GNNs) and their explainability are experiencing rapid developments. However, there is neither a unified treatment of GNN explainability methods, nor a standard benchmark and testbed for evaluations. In this survey, we provide a unified and taxonomic view of current GNN explainability methods. -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll open source more research projects in this way. It trains much faster. ... -
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CleverHans
An adversarial example library for constructing attacks
This repository contains the source code for CleverHans, a Python library to benchmark machine learning systems' vulnerability to adversarial examples. You can learn more about such vulnerabilities on the accompanying blog. The CleverHans library is under continual development, always welcoming contributions of the latest attacks and defenses. In particular, we always welcome help with resolving the issues currently open. -
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CRSLab
CRSLab is an open-source toolkit
CRSLab is an open-source toolkit for building Conversational Recommender System (CRS). It is developed based on Python and PyTorch. CRSLab has the following highlights. Comprehensive benchmark models and datasets: We have integrated commonly-used 6 datasets and 18 models, including graph neural network and pre-training models such as R-GCN, BERT and GPT-2. We have preprocessed these datasets to support these models, and release for downloading. Extensive and standard evaluation protocols: We support a series of widely-adopted evaluation protocols for testing and comparing different CRS. ... -
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XLM (Cross-lingual Language Model)
PyTorch original implementation of Cross-lingual Language Model
...Using a shared subword vocabulary, XLM learns language-agnostic features that work well for classification and sequence labeling tasks such as XNLI, NER, and POS without target-language supervision. The repository provides preprocessing pipelines, training code, and fine-tuning scripts so you can reproduce benchmark results or adapt models to your own multilingual corpora. Pretrained checkpoints cover dozens of languages and multiple model sizes, balancing quality and compute needs. -
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Awesome Graph Classification
Graph embedding, classification and representation learning papers
A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Relevant graph classification benchmark datasets are available. Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations. -
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MachineLearningStocks
Using python and scikit-learn to make stock predictions
...Using libraries such as pandas and scikit-learn, the repository shows how historical financial indicators can be transformed into machine learning features. The model attempts to predict whether specific stocks will outperform a benchmark index such as the S&P 500. The repository includes scripts for parsing financial statistics, building training datasets, and performing backtesting to evaluate model performance over historical periods. Because it is structured as a template project, developers are encouraged to extend or modify the pipeline to test different algorithms, features, or investment strategies. -
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gradslam
gradslam is an open source differentiable dense SLAM library
gradslam is an open-source framework providing differentiable building blocks for simultaneous localization and mapping (SLAM) systems. We enable the usage of dense SLAM subsystems from the comfort of PyTorch. The question of “representation” is central in the context of dense simultaneous localization and mapping (SLAM). Newer learning-based approaches have the potential to leverage data or task performance to directly inform the choice of representation. However, learning representations... -
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TensorFlow 2.0 Tutorials
TensorFlow 2.x version's Tutorials and Examples
...Each section of the repository includes runnable code and structured experiments designed to illustrate how different architectures and algorithms function in real applications. The tutorials use well-known benchmark datasets such as MNIST, CIFAR, and Fashion-MNIST to demonstrate practical model training and evaluation workflows. -
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Tiny
Tiny Face Detector, CVPR 2017
...The code depends on MatConvNet, which must be compiled (with GPU / CUDA / cuDNN support) for full performance. Pretrained model provided (ResNet101-based, plus alternatives). Demo and evaluation scripts for benchmark datasets. Use of “foveal descriptors” to incorporate context for low-resolution faces. Pretrained model provided (ResNet101-based, plus alternatives). -
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NLP-progress
Repository to track the progress in Natural Language Processing (NLP)
...It aims to cover both traditional and core NLP tasks such as dependency parsing and part-of-speech tagging as well as more recent ones such as reading comprehension and natural language inference. The main objective is to provide the reader with a quick overview of benchmark datasets and the state-of-the-art for their task of interest, which serves as a stepping stone for further research. To this end, if there is a place where results for a task are already published and regularly maintained, such as a public leaderboard, the reader will be pointed there. -
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SMAC
SMAC: The StarCraft Multi-Agent Challenge
SMAC (StarCraft II Multi-Agent Challenge) is a benchmark environment for cooperative multi-agent reinforcement learning (MARL), based on real-time strategy (RTS) game scenarios in StarCraft II. It allows researchers to test algorithms where multiple units (agents) must collaborate to win battles against built-in game AI opponents. SMAC provides a controlled testbed for studying decentralized execution and centralized training paradigms in MARL. -
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Merge PDF Files
It is a Windows library that merges standard PDFs into a final PDF
...You can send the input PDFs (by file name or by byte array) and you can have the final PDF (saved on a file or get back on a byte array). The library calls can be synchronous or asynchronous. We want to give you a benchmark, the library was used to create a PDF from single page(scanned) image by an OCR SDK (it is not included in our library, you can use any on the market): 20,000 Images (the OCR SDK creates single page PDF text searchable, running 50 threads) in 80 minutes. The size of the final PDF searchable was 800Mb. If you download the library, we provide a sample which cover all the scenarios possible (synchronous and asynchronous). -
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maskrcnn-benchmark
Fast, modular reference implementation of Instance Segmentation
Mask R-CNN Benchmark is a PyTorch-based framework that provides high-performance implementations of object detection, instance segmentation, and keypoint detection models. Originally built to benchmark Mask R-CNN and related models, it offers a clean, modular design to train and evaluate detection systems efficiently on standard datasets like COCO. -
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Dopamine
Framework for prototyping of reinforcement learning algorithms
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). This first version focuses on supporting the state-of-the-art, single-GPU Rainbow agent (Hessel et al., 2018) applied to Atari 2600 game-playing (Bellemare et al., 2013). Specifically, our Rainbow agent implements the three components identified as most important... -
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SSD
A PyTorch Implementation of Single Shot MultiBox Detector
...The repository includes the major components needed for an object detection workflow, including training scripts, evaluation scripts, demos, and utility modules. It supports commonly used benchmark datasets such as PASCAL VOC and MS COCO, and it also provides scripts to simplify downloading and setting up those datasets. For training visibility, the project includes support for Visdom so users can monitor loss in real time through a browser-based interface. Its structure makes it useful both as a reference implementation for learning SSD and as a base for custom experimentation in detection research or practical computer vision projects. -
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Prolog+CG is a Java implementation of Prolog with extensions implementing a subset of the Conceptual Graph (CG) theory of John Sowa. CGs are first-class datatypes on a par with terms. Object oriented extensions are also included.
