Classification datasets results

Classification datasets results


What is the class of this image ?

Discover the current state of the art in objects classification.

MNIST
who is the best in MNIST ?

MNIST 50 results collected

Units: error %

Classify handwriten digits. Some additional results are available on the original dataset page.

Result Method Venue Details
0.21% Regularization of Neural Networks using DropConnect ICML 2013
0.23% Multi-column Deep Neural Networks for Image Classification CVPR 2012
0.23% APAC: Augmented PAttern Classification with Neural Networks arXiv 2015
0.24% Batch-normalized Maxout Network in Network arXiv 2015 Details
0.29% Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree AISTATS 2016 Details
0.31% Recurrent Convolutional Neural Network for Object Recognition CVPR 2015
0.31% On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units arXiv 2015
0.32% Fractional Max-Pooling arXiv 2015 Details
0.33% Competitive Multi-scale Convolution arXiv 2015
0.35% Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition Neural Computation 2010 Details
0.35% C-SVDDNet: An Effective Single-Layer Network for Unsupervised Feature Learning arXiv 2014
0.37% Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network arXiv 2015 Details
0.39% Efficient Learning of Sparse Representations with an Energy-Based Model NIPS 2006 Details
0.39% Convolutional Kernel Networks arXiv 2014 Details
0.39% Deeply-Supervised Nets arXiv 2014
0.4% Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis Document Analysis and Recognition 2003
0.40% Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks arXiv 2015
0.42% Multi-Loss Regularized Deep Neural Network CSVT 2015 Details
0.45% Maxout Networks ICML 2013 Details
0.45% Training Very Deep Networks NIPS 2015 Details
0.45% ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks arXiv 2015
0.46% Deep Convolutional Neural Networks as Generic Feature Extractors IJCNN 2015 Details
0.47% Network in Network ICLR 2014 Details
0.52 % Trainable COSFIRE filters for keypoint detection and pattern recognition PAMI 2013 Details
0.53% What is the Best Multi-Stage Architecture for Object Recognition? ICCV 2009 Details
0.54% Deformation Models for Image Recognition PAMI 2007 Details
0.54% A trainable feature extractor for handwritten digit recognition Journal Pattern Recognition 2007 Details
0.56% Training Invariant Support Vector Machines Machine Learning 2002 Details
0.59% Simple Methods for High-Performance Digit Recognition Based on Sparse Coding TNN 2008 Details
0.62% Unsupervised learning of invariant feature hierarchies with applications to object recognition CVPR 2007 Details
0.62% PCANet: A Simple Deep Learning Baseline for Image Classification? arXiv 2014 Details
0.63% Shape matching and object recognition using shape contexts PAMI 2002 Details
0.64% Beyond Spatial Pyramids: Receptive Field Learning for Pooled Image Features CVPR 2012
0.68% Handwritten Digit Recognition using Convolutional Neural Networks and Gabor Filters ICCI 2003
0.69% On Optimization Methods for Deep Learning ICML 2011
0.71% Deep Fried Convnets ICCV 2015 Details
0.75% Sparse Activity and Sparse Connectivity in Supervised Learning JMLR 2013
0.78% Explaining and Harnessing Adversarial Examples ICLR 2015 Details
0.82% Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations ICML 2009
0.84% Supervised Translation-Invariant Sparse Coding CVPR 2010 Details
0.94% Large-Margin kNN Classification using a Deep Encoder Network 2009
0.95% Deep Boltzmann Machines AISTATS 2009
1.01% BinaryConnect: Training Deep Neural Networks with binary weights during propagations NIPS 2015 Details
1.1% StrongNet: mostly unsupervised image recognition with strong neurons technical report on ALGLIB website 2014 Details
1.12% CS81: Learning words with Deep Belief Networks 2008
1.19% Convolutional Neural Networks 2003 Details
1.2% Reducing the dimensionality of data with neural networks 2006
1.40% Convolutional Clustering for Unsupervised Learning arXiv 2015 Details
1.5% Deep learning via semi-supervised embedding 2008
14.53% Deep Representation Learning with Target Coding AAAI 2015
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CIFAR-10
who is the best in CIFAR-10 ?

CIFAR-10 49 results collected

Units: accuracy %

Classify 32x32 colour images.

Result Method Venue Details
96.53% Fractional Max-Pooling arXiv 2015 Details
95.59% Striving for Simplicity: The All Convolutional Net ICLR 2015 Details
94.16% All you need is a good init ICLR 2016 Details
94% Lessons learned from manually classifying CIFAR-10 unpublished 2011 Details
93.95% Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree AISTATS 2016 Details
93.72% Spatially-sparse convolutional neural networks arXiv 2014
93.63% Scalable Bayesian Optimization Using Deep Neural Networks ICML 2015
93.57% Deep Residual Learning for Image Recognition arXiv 2015 Details
93.45% Fast and Accurate Deep Network Learning by Exponential Linear Units arXiv 2015 Details
93.34% Universum Prescription: Regularization using Unlabeled Data arXiv 2015
93.25% Batch-normalized Maxout Network in Network arXiv 2015 Details
93.13% Competitive Multi-scale Convolution arXiv 2015
92.91% Recurrent Convolutional Neural Network for Object Recognition CVPR 2015 Details
92.49% Learning Activation Functions to Improve Deep Neural Networks ICLR 2015 Details
92.45% cifar.torch unpublished 2015 Details
92.40% Training Very Deep Networks NIPS 2015 Details
92.23% Stacked What-Where Auto-encoders arXiv 2015
91.88% Multi-Loss Regularized Deep Neural Network CSVT 2015 Details
91.78% Deeply-Supervised Nets arXiv 2014 Details
91.73% BinaryConnect: Training Deep Neural Networks with binary weights during propagations NIPS 2015 Details
91.48% On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units arXiv 2015
91.40% Spectral Representations for Convolutional Neural Networks NIPS 2015
91.2% Network In Network ICLR 2014 Details
91.19% Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves IJCAI 2015 Details
90.78% Deep Networks with Internal Selective Attention through Feedback Connections NIPS 2014 Details
90.68% Regularization of Neural Networks using DropConnect ICML 2013
90.65% Maxout Networks ICML 2013 Details
90.61% Improving Deep Neural Networks with Probabilistic Maxout Units ICLR 2014 Details
90.5% Practical Bayesian Optimization of Machine Learning Algorithms NIPS 2012 Details
89.67% APAC: Augmented PAttern Classification with Neural Networks arXiv 2015
89.14% Deep Convolutional Neural Networks as Generic Feature Extractors IJCNN 2015 Details
89% ImageNet Classification with Deep Convolutional Neural Networks NIPS 2012 Details
88.80% Empirical Evaluation of Rectified Activations in Convolution Network ICML workshop 2015 Details
88.79% Multi-Column Deep Neural Networks for Image Classification CVPR 2012 Details
87.65% ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks arXiv 2015
86.70 % An Analysis of Unsupervised Pre-training in Light of Recent Advances ICLR 2015 Details
84.87% Stochastic Pooling for Regularization of Deep Convolutional Neural Networks arXiv 2013
84.4% Improving neural networks by preventing co-adaptation of feature detectors arXiv 2012 Details
83.96% Discriminative Learning of Sum-Product Networks NIPS 2012
82.9% Stable and Efficient Representation Learning with Nonnegativity Constraints ICML 2014 Details
82.2% Learning Invariant Representations with Local Transformations ICML 2012 Details
82.18% Convolutional Kernel Networks arXiv 2014 Details
82% Discriminative Unsupervised Feature Learning with Convolutional Neural Networks NIPS 2014 Details
80.02% Learning Smooth Pooling Regions for Visual Recognition BMVC 2013
80% Object Recognition with Hierarchical Kernel Descriptors CVPR 2011
79.7% Learning with Recursive Perceptual Representations NIPS 2012 Details
79.6 % An Analysis of Single-Layer Networks in Unsupervised Feature Learning AISTATS 2011 Details
78.67% PCANet: A Simple Deep Learning Baseline for Image Classification? arXiv 2014 Details
75.86% Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network arXiv 2015 Details
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CIFAR-100
who is the best in CIFAR-100 ?

CIFAR-100 31 results collected

Units: accuracy %

Classify 32x32 colour images.

Result Method Venue Details
75.72% Fast and Accurate Deep Network Learning by Exponential Linear Units arXiv 2015 Details
75.7% Spatially-sparse convolutional neural networks arXiv 2014
73.61% Fractional Max-Pooling arXiv 2015 Details
72.60% Scalable Bayesian Optimization Using Deep Neural Networks ICML 2015
72.44% Competitive Multi-scale Convolution arXiv 2015
72.34% All you need is a good init ICLR 2015 Details
71.14% Batch-normalized Maxout Network in Network arXiv 2015 Details
70.80% On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units arXiv 2015
69.17% Learning Activation Functions to Improve Deep Neural Networks ICLR 2015 Details
69.12% Stacked What-Where Auto-encoders arXiv 2015
68.53% Multi-Loss Regularized Deep Neural Network CSVT 2015 Details
68.40% Spectral Representations for Convolutional Neural Networks NIPS 2015
68.25% Recurrent Convolutional Neural Network for Object Recognition CVPR 2015
67.76% Training Very Deep Networks NIPS 2015 Details
67.68% Deep Convolutional Neural Networks as Generic Feature Extractors IJCNN 2015 Details
67.63% Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree AISTATS 2016 Details
67.38% HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition ICCV 2015
67.16% Universum Prescription: Regularization using Unlabeled Data arXiv 2015
66.29% Striving for Simplicity: The All Convolutional Net ICLR 2014
66.22% Deep Networks with Internal Selective Attention through Feedback Connections NIPS 2014
65.43% Deeply-Supervised Nets arXiv 2014 Details
64.77% Deep Representation Learning with Target Coding AAAI 2015
64.32% Network in Network ICLR 2014 Details
63.15% Discriminative Transfer Learning with Tree-based Priors NIPS 2013 Details
61.86% Improving Deep Neural Networks with Probabilistic Maxout Units ICLR 2014
61.43% Maxout Networks ICML 2013 Details
60.8% Stable and Efficient Representation Learning with Nonnegativity Constraints ICML 2014 Details
59.75% Empirical Evaluation of Rectified Activations in Convolution Network ICML workshop 2015 Details
57.49% Stochastic Pooling for Regularization of Deep Convolutional Neural Networks arXiv 2013
56.29% Learning Smooth Pooling Regions for Visual Recognition BMVC 2013 Details
54.23% Beyond Spatial Pyramids: Receptive Field Learning for Pooled Image Features CVPR 2012
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STL-10
who is the best in STL-10 ?

STL-10 18 results collected

Units: accuracy %

Similar to CIFAR-10 but with 96x96 images. Original dataset website.

Result Method Venue Details
74.33% Stacked What-Where Auto-encoders arXiv 2015
74.10% Convolutional Clustering for Unsupervised Learning arXiv 2015 Details
73.15% Deep Representation Learning with Target Coding AAAI 2015
72.8% (±0.4%) Discriminative Unsupervised Feature Learning with Convolutional Neural Networks NIPS 2014 Details
70.20 % (±0.7 %) An Analysis of Unsupervised Pre-training in Light of Recent Advances ICLR 2015 Details
70.1% (±0.6%) Multi-Task Bayesian Optimization NIPS 2013 Details
68.23% ± 0.5 C-SVDDNet: An Effective Single-Layer Network for Unsupervised Feature Learning arXiv 2014
68% (±0.55%) Committees of deep feedforward networks trained with few data arXiv 2014
67.9% (±0.6%) Stable and Efficient Representation Learning with Nonnegativity Constraints ICML 2014 Details
64.5% (±1%) Unsupervised Feature Learning for RGB-D Based Object Recognition ISER 2012 Details
62.32% Convolutional Kernel Networks arXiv 2014 Details
62.3% (±1%) Discriminative Learning of Sum-Product Networks NIPS 2012
61.0% (±0.58%) No more meta-parameter tuning in unsupervised sparse feature learning arXiv 2014
61% Deep Learning of Invariant Features via Simulated Fixations in Video NIPS 2012 2012
60.1% (±1%) Selecting Receptive Fields in Deep Networks NIPS 2011
58.7% Learning Invariant Representations with Local Transformations ICML 2012
58.28% Pooling-Invariant Image Feature Learning arXiv 2012 Details
56.5% Deep Learning of Invariant Features via Simulated Fixations in Video NIPS 2012 Details
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SVHN
who is the best in SVHN ?

SVHN 17 results collected

Units: error %

The Street View House Numbers (SVHN) Dataset.

SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST(e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images.

Result Method Venue Details
1.69% Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree AISTATS 2016 Details
1.76% Competitive Multi-scale Convolution arXiv 2015
1.77% Recurrent Convolutional Neural Network for Object Recognition CVPR 2015 Details
1.81% Batch-normalized Maxout Network in Network arXiv 2015 Details
1.92% Deeply-Supervised Nets arXiv 2014
1.92% Multi-Loss Regularized Deep Neural Network CSVT 2015 Details
1.94% Regularization of Neural Networks using DropConnect ICML 2013
1.97% On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units arXiv 2015
2% Estimated human performance NIPS 2011 Details
2.15% BinaryConnect: Training Deep Neural Networks with binary weights during propagations NIPS 2015
2.16% Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks ICLR 2014 Details
2.35% Network in Network ICLR 2014 Details
2.38% ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks arXiv 2015
2.47% Maxout Networks ICML 2013 Details
2.8% Stochastic Pooling for Regularization of Deep Convolutional Neural Networks arXiv 2013 Details
3.96% Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network arXiv 2015 Details
4.9% Convolutional neural networks applied to house numbers digit classification ICPR 2012 Details
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ILSVRC2012 task 1
who is the best in ILSVRC2012 task 1 ?

ILSVRC2012 task 1

Units: Error (5 guesses)

1000 categories classification challenge. With tens of thousands of training, validation and testing images.

See this interesting comparative analysis.

Results are collected in the following external webpage