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This project gathers information and scripts that references the location of datasets that can be used for Data Analytics. These standard datasets, which are often used by researchers
or may come from use cases. All these datasets can be used to gain expertise.
CIFAR-10: Computer-vision images dataset used for object recognition
- Description: 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.
- Size: 163 MB (python version)
- License: If you're going to use this dataset, please cite the tech report: Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009.
- url: https://www.cs.toronto.edu/~kriz/cifar.html
- Download url:https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz, md5sum: c58f30108f718f92721af3b95e74349a
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ImageNet: Large Scale Visual Recognition Challenge 2012 (ILSVRC2012)
- Description: Training data 1,281,167 images in 1000 synsets. Validation data 50 images/synset. Test data 100 images/synset.
Training images (Task 1 & 2). 138GB. MD5: 1d675b47d978889d74fa0da5fadfb00e
Training images (Task 3). 728MB. MD5: ccaf1013018ac1037801578038d370da
Validation images (all tasks). 6.3GB. MD5: 29b22e2961454d5413ddabcf34fc5622
Test images (all tasks). 13GB. MD5: fe64ceb247e473635708aed23ab6d839
Terms of use: by downloading the image data from the above URLs, you agree to the following terms:
You will use the data only for non-commercial research and educational purposes.
You will NOT distribute the above URL(s).p
Stanford University and Princeton University make no representations or warranties regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose.
You accept full responsibility for your use of the data and shall defend and indemnify Stanford University and Princeton University, including their employees, officers and agents, against any and all claims arising from your use of the data, including but not limited to your use of any copies of copyrighted images that you may create from the data.
- url: http://www.image-net.org/challenges/LSVRC/2012
- Download url: