There are 2 repositories under benchmark-datasets topic.
A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning.
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
Supplementary material for our paper "THERE IS NO DATA LIKE MORE DATA" is provided.
This repository contains the collection of UCI (real-life) datasets and Synthetic (artificial) datasets (with cluster labels and MATLAB files) ready to use with clustering algorithms.
This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.
benchmark dataset and Deep learning method (Hierarchical Interaction Network, HINT) for clinical trial approval probability prediction, published in Cell Patterns 2022.
Launched in March 2020 in response to the coronavirus disease 2019 (COVID-19) pandemic, COVID-Net is a global open source, open access initiative dedicated to accelerating advancement in machine learning to aid front-line healthcare workers and clinical institutions around the world fighting the continuing pandemic. Towards this goal, our global multi-disciplinary team of researchers, developers, and clinicians have made publicly available a suite of tailored deep neural network models for tackling different challenges ranging from screening to risk stratification to treatment planning for patients with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Furthermore, we have made available fully curated, open access benchmark datasets comprised of some of the largest, most diverse patient cohorts from around the world.
A Python toolkit for setting up benchmarking dataset using biomedical networks
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
Diving Deeper into Underwater Image Enhancement: A Survey, accepted in Signal Processing: Image Communication.
A framework for benchmarking clustering algorithms – Benchmark suite, version 1
FDSML Course Project 2020/21
Benchmarking dataset of degraded word images (with character splits) in Kannada along with their associated ground truth Unicode text
Benchmarking dataset of merged symbols in Kannada along with their associated ground truth Unicode text
A Rust port of NASBench: https://github.com/google-research/nasbench
A framework for benchmarking clustering algorithms – Benchmark results (for version 1 of the Suite)
Classical benchmark sets for the one-dimensional bin packing problem
Estonian Grammatical Error Correction (GEC) test and development corpus that contains L2 learner texts error-annotated in the M2 format.
Large benchmark data for 4kills/go-zlib and 4kills/go-libdeflate, removed from the original go library/repository itself to minimize library size.
Source code for experiments in the papers "Beyond Benchmarks: Assessing Knowledge Graph Completion Methods on Non-Benchmark Employee Data" (IEEE 2024, yet to be published)
A tool to translate Argoverse into KITTI dataset format
Notebooks gerados para o meu TCC no curso de graduação Sistemas e Mídias Digitais da Universidade Federal do Ceará.