Английская Википедия:CIFAR-10
Шаблон:Short description The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research.[1][2] The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes.[3] The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. There are 6,000 images of each class.[4]
Computer algorithms for recognizing objects in photos often learn by example. CIFAR-10 is a set of images that can be used to teach a computer how to recognize objects. Since the images in CIFAR-10 are low-resolution (32x32), this dataset can allow researchers to quickly try different algorithms to see what works.
CIFAR-10 is a labeled subset of the 80 Million Tiny Images dataset from 2008, published in 2009. When the dataset was created, students were paid to label all of the images.[5]
Various kinds of convolutional neural networks tend to be the best at recognizing the images in CIFAR-10.
Research papers claiming state-of-the-art results on CIFAR-10
This is a table of some of the research papers that claim to have achieved state-of-the-art results on the CIFAR-10 dataset. Not all papers are standardized on the same pre-processing techniques, like image flipping or image shifting. For that reason, it is possible that one paper's claim of state-of-the-art could have a higher error rate than an older state-of-the-art claim but still be valid.
| Paper title | Error rate (%) | Publication date |
|---|---|---|
| Convolutional Deep Belief Networks on CIFAR-10[6] | 21.1 | August, 2010 |
| Maxout Networks[7] | 9.38 | Шаблон:Dts |
| Wide Residual Networks[8] | 4.0 | Шаблон:Dts |
| Neural Architecture Search with Reinforcement Learning[9] | 3.65 | Шаблон:Dts |
| Fractional Max-Pooling[10] | 3.47 | Шаблон:Dts |
| Densely Connected Convolutional Networks[11] | 3.46 | Шаблон:Dts |
| Shake-Shake regularization[12] | 2.86 | Шаблон:Dts |
| Coupled Ensembles of Neural Networks[13] | 2.68 | Шаблон:Dts |
| ShakeDrop regularization[14] | 2.67 | Feb 7, 2018 |
| Improved Regularization of Convolutional Neural Networks with Cutout[15] | 2.56 | Aug 15, 2017 |
| Regularized Evolution for Image Classifier Architecture Search[16] | 2.13 | Feb 6, 2018 |
| Rethinking Recurrent Neural Networks and other Improvements for Image Classification[17] | 1.64 | July 31, 2020 |
| AutoAugment: Learning Augmentation Policies from Data[18] | 1.48 | May 24, 2018 |
| A Survey on Neural Architecture Search[19] | 1.33 | May 4, 2019 |
| GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism[20] | 1.00 | Nov 16, 2018 |
| Reduction of Class Activation Uncertainty with Background Information[21] | 0.95 | May 5, 2023 |
| An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale[22] | 0.5 | 2021 |
Benchmarks
CIFAR-10 is also used as a performance benchmark for teams competing to run neural networks faster and cheaper. DAWNBench has benchmark data on their website.
See also
References
External links
- CIFAR-10 page - The home of the dataset
- Canadian Institute For Advanced Research
Similar datasets
- CIFAR-100: Similar to CIFAR-10 but with 100 classes and 600 images each.
- ImageNet (ILSVRC): 1 million color images of 1000 classes. Imagenet images are higher resolution, averaging 469x387 resolution.
- Street View House Numbers (SVHN): Approximately 600,000 images of 10 classes (digits 0-9). Also 32x32 color images.
- 80 million tiny images dataset: CIFAR-10 is a labeled subset of this dataset.
Шаблон:Differentiable computing
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