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Benchmarking

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/vztu/VIDEVAL_release

UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content

https://github.com/w-sugar/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/wMito/HuSc3D

HuSc3D: Human Sculpture dataset for 3D object reconstruction

https://github.com/wangjw6/llmob

OpenAGI: When LLM Meets Domain Experts

https://github.com/wasserth/totalsegmentator

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

https://github.com/wendelinboehmer/dcg

The StarCraft Multi-Agent Challenge

https://github.com/wenhuchen/data-to-text-evaluation-metric

CIDEr: Consensus-based Image Description Evaluation

https://github.com/wenlai-lavine/jola

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

https://github.com/wenyueh/llm-recsys-id

OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems

https://github.com/westny/neural-stability

Which priors matter? Benchmarking models for learning latent dynamics

https://github.com/wmdataphys/FM4DIRC

Generative Models for Fast Simulation of Cherenkov Detectors at the Electron-Ion Collider

https://github.com/wsx66848/detection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/wuxx1016/Reinforcement-Learning-in-Finance

QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds

https://github.com/wyndwarrior/imitation_from_observation

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/xingyu-lin/softagent

SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation

https://github.com/xqnwang/fuma

GRATIS: GeneRAting TIme Series with diverse and controllable characteristics

https://github.com/xue-pai/FuxiCTR

BARS: Towards Open Benchmarking for Recommender Systems

https://github.com/xy-guo/mmdetection_kitti

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/yan-gao-GY/FedScale

FedScale: Benchmarking Model and System Performance of Federated Learning at Scale

https://github.com/yangletliu/finlora

FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets

https://github.com/yangyanli/PointCNN

PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding

https://github.com/yanmeizhao/MyMmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/yaodongyu/projnorm

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

https://github.com/yaq007/cleverhans

Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

https://github.com/yaroslav-moiseev/evidence-based-possibly-best-practices-in-classical-ML

A Meta-Analysis of the Anomaly Detection Problem

https://github.com/yashkotadia/GatedGCN-PATTERN

Benchmarking Graph Neural Networks

https://github.com/yifan-fanyi/Pixelhop

PixelHop: A Successive Subspace Learning (SSL) Method for Object Classification

https://github.com/yifan-fanyi/myMCL-Realization

PixelHop: A Successive Subspace Learning (SSL) Method for Object Classification

https://github.com/yinanhe/ForgeryNet

ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis

https://github.com/yizhongw/tk-instruct

Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

https://github.com/ylqi/count-anything

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/yolo0226/mmdetection-master

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/yongbowin/MSMARCO-Question-Answering_annotation

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

https://github.com/yongbowin/OpenKP_annotation

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

https://github.com/yoonkim/neural-qcfg

StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer

https://github.com/yorkerlin/remove-the-square-root

On Empirical Comparisons of Optimizers for Deep Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.