Datasets:
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
'4': E
'5': F
'6': G
'7': H
'8': I
'9': J
splits:
- name: train
num_bytes: 6842235.510231657
num_examples: 14979
- name: test
num_bytes: 1715013.5296924065
num_examples: 3745
download_size: 8865158
dataset_size: 8557249.039924063
Dataset Card for "notMNIST"
Overview
The notMNIST dataset is a collection of images of letters from A to J in various fonts. It is designed as a more challenging alternative to the traditional MNIST dataset, which consists of handwritten digits. The notMNIST dataset is commonly used in machine learning and computer vision tasks for character recognition.
Dataset Information
Number of Classes: 10 (A to J) Number of Samples: 187,24 Image Size: 28 x 28 pixels Color Channels: Grayscale
Dataset Structure
The dataset is split into a training set and a test set. Each class has its own subdirectory containing images of that class. The directory structure is as follows:
notMNIST/ |-- train/ | |-- A/ | |-- B/ | |-- ... | |-- J/ | |-- test/ | |-- A/ | |-- B/ | |-- ... | |-- J/
Acknowledgements
http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html https://www.kaggle.com/datasets/lubaroli/notmnist
Inspiration
This is a pretty good dataset to train classifiers! According to Yaroslav:
Judging by the examples, one would expect this to be a harder task than MNIST. This seems to be the case -- logistic regression on top of stacked auto-encoder with fine-tuning gets about 89% accuracy whereas same approach gives got 98% on MNIST. Dataset consists of small hand-cleaned part, about 19k instances, and large uncleaned dataset, 500k instances. Two parts have approximately 0.5% and 6.5% label error rate. I got this by looking through glyphs and counting how often my guess of the letter didn't match it's unicode value in the font file.