OpenIMDS

Open Image Datasets

This simple header only C library can read popular classification datasets.

1. Classification datasets

We are using IMDSImage type for each datasets. It has width,height,the number of images,images and labels only.

MNIST(mnist.h)

The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.

It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.

This script automatically download MNIST datasets to your temporary directory at once. So you just need below 2 functions.

The default parameters are only available in C++. It automatically disable in C.

IMDSImage GetMnistTrainData(int padding=0,bool bias=true,IMDSFormat format=RRGGBB);

IMDSImage GetMnistValidData(int padding=0,bool bias=true,IMDSFormat format=RRGGBB);`

• Parameters

  • padding : The top,bottom,left and right padding of image.
  • bias : Adding bias input on image if this value set true.
  • format : Image array order type.

Example

IMDSImage train= GetMnistTrainData(0, 1 / 255.0F,true);
IMDSImage valid= GetMnistValidData(0, 1 / 255.0F,true);

Fashion(mnist.h)

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

IMDSImage GetFashionTrainData(int padding=0,bool bias=true,IMDSFormat format=RRGGBB)

IMDSImage GetFashionValidData(int padding=0,bool bias=true,IMDSFormat format=RRGGBB)

Cifar-10(cifar10.h)

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.

IMDSImage GetCifar10TrainData(int padding=0,bool bias=true,IMDSFormat format=RRGGBB)

IMDSImage GetCifar10ValidData(int padding=0,bool bias=true,IMDSFormat format=RRGGBB)

char* GetCifar10Class(int index)

STL-10(stl10.h)

The STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. It is inspired by the CIFAR-10 dataset but with some modifications. In particular, each class has fewer labeled training examples than in CIFAR-10, but a very large set of unlabeled examples is provided to learn image models prior to supervised training. The primary challenge is to make use of the unlabeled data (which comes from a similar but different distribution from the labeled data) to build a useful prior. We also expect that the higher resolution of this dataset (96x96) will make it a challenging benchmark for developing more scalable unsupervised learning methods.

IMDSImage GetSTL10TrainData(int padding=0,bool bias=true,IMDSFormat format=RRGGBB)

IMDSImage GetSTL10ValidData(int padding=0,bool bias=true,IMDSFormat format=RRGGBB)

char* GetSTL10Class(int index)