Database Collection

In this article, I will collect some useful databases for Image Classification, Segmentation and Object Detection.

FAMOUS

• CIFAR
  CIFAR-10: 60000 color images (32x32) in 10 classes (6000 images per class). 50000 training images and 10000 test images.
  CIFAR-100: 100 classes and 600 images each. 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a “fine” label (the class to which it belongs) and a “coarse” label (the superclass to which it belongs).
• MNIST
  MNIST database contains handwritten digits with 60,000 training images and 10,000 testing images.
• ImageNet
  ImageNet is an image database organized according to the WordNet hierarchy. It has over 20,000 classes and totally more that 14 million images.
• SVHN (Street View House Numbers)

  SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST(e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images.

• STL-10
  STL-10 has 10 classes, 500 training images (10 pre-defined folds), and 800 test images per class. This database can be used for developing unsupervised feature learning, deep learning, self-taught learning algorithms.
• PASCAL VOC2012 dataset

  Face

• Labeled Faces in the Wild (LWF): http://vis-www.cs.umass.edu/lfw/
• CUHK Face Alignment Database: http://mmlab.ie.cuhk.edu.hk/archive/CNN_FacePoint.htm
• Annotated Facial Landmarks in the Wild: https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/#database
• Multi-Task Facial Landmark: https://www.safaribooksonline.com/library/view/deep-learning-for/9781788295628/001f4f21-ab6e-48e1-8623-8a0ec35fcce9.xhtml
• Large-scale CelebFaces Attributes: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
• CBCL Face Database: http://cbcl.mit.edu/software-datasets/FaceData2.html

  Pedestrian

• CUHK Occlusion Dataset: http://mmlab.ie.cuhk.edu.hk/datasets/cuhk_occlusion/index.html
• CUHK Person Re-identification Datasets: http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html
• CBCL Pedestrian Database: http://cbcl.mit.edu/software-datasets/PedestrianData.html
• INRIA Person Dataset: http://pascal.inrialpes.fr/data/human/

  Fashion

• Large-scale Fashion (DeepFashion) Database: http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html
• Color-Fashion Dataset: https://sites.google.com/site/fashionparsing/dataset

  Car

• CBCL Car Database: http://cbcl.mit.edu/software-datasets/CarData.html
• INRIA Car Data Set:
• Toyota Motor Europe (TME) Motorway Dataset: http://cmp.felk.cvut.cz/data/motorway/

Updating……