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MNIST

ImagesUnknownIntroduced 1998-11-01

The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger NIST Special Database 3 (digits written by employees of the United States Census Bureau) and Special Database 1 (digits written by high school students) which contain monochrome images of handwritten digits. The digits have been size-normalized and centered in a fixed-size image. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.

Source: http://yann.lecun.com/exdb/mnist/ Image Source: https://en.wikipedia.org/wiki/MNIST_database#/media/File:MnistExamples.png

Benchmarks

Adversarial Defense/Accuracy Adversarial Defense/Inference speed Anomaly Detection/ROC AUC Anomaly Detection/AUROC Anomaly Detection/AUC-ROC AutoML/R2 Classification/Accuracy Clustering Algorithms Evaluation/ARI Clustering Algorithms Evaluation/F1-score Clustering Algorithms Evaluation/NMI Core set discovery/F1(10-fold)Deep Clustering/NMI Density Estimation/NLL (bits/dim)Density Estimation/Log-likelihood (nats)Density Estimation/MMD-L2 Density Estimation/COV-L2 Density Estimation/NLL Federated Learning/ACC@1-50Clients Federated Learning/ACC@1-100Clients Federated Learning/ACC@1-500Clients Few-Shot Learning/Accuracy Fine-Grained Image Classification/Accuracy General Classification/Accuracy Graph Classification/Accuracy Image Classification/Percentage error Image Classification/Accuracy Image Classification/Trainable Parameters Image Classification/Cross Entropy Loss Image Classification/Epochs Image Classification/Top 1 Accuracy Image Clustering/Accuracy Image Clustering/NMI Image Generation/bits/dimension Image Generation/FID Image Generation/Precision Image Generation/Recall Image Generation/PSNR Image Generation/SSIM Meta-Learning/Accuracy Nature-Inspired Optimization Algorithm/training time (s)Network Pruning/Avg #Steps Neural Architecture Search/R2 One-Shot Learning/Accuracy Optical Character Recognition (OCR)/Accuracy Optical Character Recognition (OCR)/PERCENTAGE ERROR Stochastic Optimization/NLL Structured Prediction/Negative CLL Unsupervised Anomaly Detection/AUROC Unsupervised Anomaly Detection/AUC-ROC Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly/AUC-ROC Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly/AUC-ROC Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly/AUC-ROC Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly/AUC-ROC Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly/AUC-ROC

Related Benchmarks

MNIST Large Scale dataset/Image Classification/Average Accuracy MNIST vs Fake MNIST/Two-sample testing/Avg accuracy MNIST-M-to-MNIST/Domain Adaptation/Accuracy MNIST-full/Image Clustering/Accuracy MNIST-full/Image Clustering/NMI MNIST-rot-12/Image Classification/Test Error MNIST-rot-12k (DA)/Image Classification/Test Error MNIST-test/Anomaly Detection/F1 score MNIST-test/Image Clustering/Accuracy MNIST-test/Image Clustering/NMI MNIST-to-MNIST-M/Domain Adaptation/Accuracy MNIST-to-USPS/Domain Adaptation/Accuracy mnist/Image Classification/Percentage error

Statistics

Papers: 7,651
Benchmarks: 54

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Tasks

Adversarial Defense Adversarial Defense against FGSM Attack Anomaly Detection Audio Classification AutoML Automatic Speech Recognition Classification Classification with Binary Weight Network Clustering Algorithms Evaluation Continual Learning Continuously Indexed Domain Adaptation Core set discovery Deep Clustering Density Estimation Domain Adaptation Emotion Recognition Event Extraction Federated Learning Few-Shot Learning Fill Mask Fine-Grained Image Classification General Classification Graph Classification Handwritten Digit Recognition Hard-label Attack Iloko Speech Recognition Image Classification Image Clustering Image Generation Information Extraction Language Modelling Malicious Detection Meta-Learning Model Poisoning Multi Label Text Classification Multiview Clustering NER Named Entity Recognition (NER)Nature-Inspired Optimization Algorithm Network Pruning Neural Architecture Search One-Shot Learning Open Information Extraction Optical Character Recognition (OCR)PD-L1 Tumor Proportion Score Prediction POS Personalized Federated Learning Question Answering Rotated MNIST Semantic Similarity Sentence Pair Modeling Sequential Image Classification Sparse Learning and binarization Speech Emotion Recognition Speech Recognition Stochastic Optimization Structured Prediction Superpixel Image Classification TAG Text Classification Token Classification Türkçe Görüntü Altyazılama Unsupervised Anomaly Detection Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly Unsupervised Image Classification Unsupervised Image-To-Image Translation Unsupervised MNIST Video Prediction candy animation generation text-to-speech