All Projects → kensanata → Numbers

kensanata / Numbers

Licence: other
Handwritten digits, a bit like the MNIST dataset.

Projects that are alternatives of or similar to Numbers

First Steps Towards Deep Learning
This is an open sourced book on deep learning.
Stars: ✭ 376 (+469.7%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Easypr
An easy, flexible, and accurate plate recognition project for Chinese licenses in unconstrained situations.
Stars: ✭ 6,046 (+9060.61%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Trending Deep Learning
Top 100 trending deep learning repositories sorted by the number of stars gained on a specific day.
Stars: ✭ 543 (+722.73%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Awesome Ai Awesomeness
A curated list of awesome awesomeness about artificial intelligence
Stars: ✭ 268 (+306.06%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Online Relationship Learning
Unsupervised ML algorithm for predictive modeling and time-series analysis
Stars: ✭ 34 (-48.48%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Ai Simplest Network
The simplest form of an artificial neural network explained and demonstrated.
Stars: ✭ 333 (+404.55%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Tensorslow
Re-implementation of TensorFlow in pure python, with an emphasis on code understandability
Stars: ✭ 657 (+895.45%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Aidl kb
A Knowledge Base for the FB Group Artificial Intelligence and Deep Learning (AIDL)
Stars: ✭ 219 (+231.82%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Gaze Estimation
A deep learning based gaze estimation framework implemented with PyTorch
Stars: ✭ 33 (-50%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
All Classifiers 2019
A collection of computer vision projects for Acute Lymphoblastic Leukemia classification/early detection.
Stars: ✭ 22 (-66.67%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
L2c
Learning to Cluster. A deep clustering strategy.
Stars: ✭ 262 (+296.97%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Trafficvision
MIVisionX toolkit is a comprehensive computer vision and machine intelligence libraries, utilities and applications bundled into a single toolkit.
Stars: ✭ 52 (-21.21%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Machine Learning And Ai In Trading
Applying Machine Learning and AI Algorithms applied to Trading for better performance and low Std.
Stars: ✭ 258 (+290.91%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Nimtorch
PyTorch - Python + Nim
Stars: ✭ 346 (+424.24%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Echotorch
A Python toolkit for Reservoir Computing and Echo State Network experimentation based on pyTorch. EchoTorch is the only Python module available to easily create Deep Reservoir Computing models.
Stars: ✭ 231 (+250%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Deeplearning.ai
deeplearning.ai , By Andrew Ng, All video link
Stars: ✭ 625 (+846.97%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Data Science Resources
👨🏽‍🏫You can learn about what data science is and why it's important in today's modern world. Are you interested in data science?🔋
Stars: ✭ 171 (+159.09%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Free Ai Resources
🚀 FREE AI Resources - 🎓 Courses, 👷 Jobs, 📝 Blogs, 🔬 AI Research, and many more - for everyone!
Stars: ✭ 192 (+190.91%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Imageai
A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
Stars: ✭ 6,734 (+10103.03%)
Mutual labels:  artificial-intelligence, artificial-neural-networks
Quant Finance Resources
Courses, Articles and many more which can help beginners or professionals.
Stars: ✭ 36 (-45.45%)
Mutual labels:  artificial-intelligence, artificial-neural-networks

Numbers

This repository tries to create a repository of handwritten digits, much like the MNIST database of handwritten digits. In Switzerland, the handwritten digites sometimes look a bit different, which is why we undertake this effort.

There are two data sets in this repository. They are described below.

Warning: If you're using git on Windows to clone this repository, it make take a very long time because there are so many (tiny) files in it!

Set 1: manual collection

Our goal was 10,000 handwritten digits and we have met that goal! If you want to help us reach 20,000 handwritten digits, check out the tools directory.

The directory name of every contribution adheres to the following naming scheme:

  1. four digits to identify the person
  2. an underscore character
  3. two letter country code (ISO 3166 Alpha-2 codes) or XX if unknown (e.g. Switzerland is CH)
  4. age, rounded to the nearest decade (e.g. 35 to 44 years is 4) or X if unknown
  5. sex (M for man, F for woman, X for unknown, O for other)

Set 2: automatic collection

As part of a commercial project a neural network was trained on the numbers cut from a very large collection of documents. There are so many digits in this set that the data quality is lower than in the manual collection. You can find this set in the UNCATEGORIZED directory. A bit over 800,000 digits!

  • digits may be miscategorized or malformed (e.g. 554)
  • digits are both handwritten and printed (e.g. 552)
  • the distribution of digits is not uniform
  • no information is available about the authors

I'll be happy to take pull requests which fix miscategorized or malformed digits.

As for the distribution of numbers:

digit  files
  0   323945
  1    94075
  2    71820
  3    55240
  4    46143
  5    96376
  6    39868
  7    34836
  8    37124
  9    34571
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].