1 CIFAR
The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset.
1.1 CIFAR10
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.
1.2 CIFAR100
This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class.
dataset: 10000 x 3072 = 10000(test images) x 32(width) x 32(height) x 3(channels:RGB)
labels: a list of 10000 numbers in the range 0-9, labels_names in the batches.meta files.
2 MNIST
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. The digits have been size-normalized and centered in a fixed-size image of 28x28 pixels.
The MNIST database is a subset of a much larger datasetknown as the NIST Special Database 19 which contains handwritten digits and characters collected from over 500 writers.
The MNIST database was constructed from NIST's Special Database 3 and Special Database 1 which contain binary images of handwritten digits. NIST originally designated SD-3 as their training set and SD-1 as their test set.
The MNIST training set is composed of 30,000 patterns from SD-3 and 30,000 patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and 5,000 patterns from SD-1. The 60,000 pattern training set contained examples from approximately 250 writers.
size: 28(width) x 28(height)
In the original dataset each pixel of the image is represented by a value between 0 and 255, where 0 is black, 255 is white and anything in between is a different shade of grey.
MNIST is often the first dataset researchers try, they said:
dummy " If it doesn't work on MNIST, it won't work at all, Well, if it does work on MNIST, it may still fail on others."
3 KMNIST
The Kuzushiji-MNIST or KMNIST dataset contains 10 classes of hiragana(日语) characters with a resolution of 28x28 (grayscale) similar to MNIST. In total it contains 70000 images, 60000 for training and 10000 for testing.
4 FashionMNIST
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.
5 EMNIST
Extended Modified NIST (EMNIST).
Structure and Organization of the EMNIST datasets:
| Name | Classes | No. Training | No. Testing | Validation | Total |
|---|---|---|---|---|---|
| ByClass | 62 | 697,932 | 116,323 | No | 814,255 |
| ByMerge | 47 | 697,932 | 116,323 | No | 814,255 |
| Balanced | 47 | 112,800 | 18,800 | Yes | 131,600 |
| Digits | 10 | 240,000 | 40,000 | Yes | 280,000 |
| Letters | 37 | 88,800 | 14,800 | Yes | 103,600 |
| MNIST | 10 | 60,000 | 10,000 | Yes | 70,000 |
5.1 Balanced
total 131600, 47 classes, 2400 train examples and 400 test examples for each classs
131600 = 47 x (2400 + 400)
5.2 Digits
total 28000, 10 classes, 2400 train examples and 400 test examples for each classs
28000 = 10 x (2400 + 400)
5.3 Letters
total 145600, 26 classes, 5600 train examples and 800 test examples for each classs
145600 = 26 x (5600 + 800)
5.4 MNIST
total 70000, 10 classes, 6000 train examples and 1000 test examples for each classs
70000 = 10 x (6000 + 1000)
6 Chars74k
Character recognition datasets which consisting of all English alphabet letters in uppercase as well as lowercase , along with the digits 0-9
The Chars74k dataset consists of:
- 64 classes (0-9, A-Z, a-z)
- 7705 characters obtained from natural images
- 3410 hand drawn characters using a tablet PC
- 62992 synthesised characters from computer fonts
This gives a total of over 74K images.
TODO
7 Dogs
The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization.
Contents of this dataset:
- Number of categories: 120
- Number of images: 20,580
- Annotations: Class labels, Bounding boxes
TODO
8 Animals
TODO
9 Fruits360
A dataset of images containing fruits.
TODO
10 Boats
TODO
11 VOC
TODO
12 ADE20K
Contains more than 20K scene-centric images exhaustively annotated with objects and object parts. Specifically, the benchmark is divided into 20K images for training, 2K images for validation, and another batch of held-out images for testing.
It has 20210 training images and 2000 validation images.
12.1 ADEChallengeData2016
13 COCO
TODO
