Benchmarking
1504 papers with code • 1 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Benchmarking
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Wiki-40B
CropAndWeed Dataset
FluidLab
Apron Dataset
Most implemented papers
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
Learning Transferable Visual Models From Natural Language Supervision
openai/CLIP
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State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
zalandoresearch/fashion-mnist
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We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70, 000 fashion products from 10 categories, with 7, 000 images per category.
CIDEr: Consensus-based Image Description Evaluation
We propose a novel paradigm for evaluating image descriptions that uses human consensus.
The StarCraft Multi-Agent Challenge
oxwhirl/pymarl
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In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.
Benchmarking Graph Neural Networks
graphdeeplearning/benchmarking-gnns
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In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.
Benchmarking Deep Reinforcement Learning for Continuous Control
rllab/rllab
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Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning.
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
An adversarial example library for constructing attacks, building defenses, and benchmarking both
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
IBM/AIF360
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Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
hendrycks/robustness
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ICLR 2019
Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations.
