Deep learning datasets (DLDS)

The purpose of DLDS is to make fetching and preparing datasets an automatic and painless process.

• Necessary resources are automatically downloaded and checked for integrity.
• Datasets are processed into HDF5 files, which can be read using a variety of languages including Lua, Python, and Matlab.
• Class labels all use 1-based indexing

Building

1. Copy config.example.json to config.json and customise to your liking
2. Install Docker
3. Build the DLDS Docker image with docker build -t dlds $PWD

Usage

Example: Installing the MNIST data set.

docker run --rm -it --volume=/data:/data dlds install mnist

Ensure that you set the volume(s) to match your particular config.json. The command shown here works with the example config file.

Supported datasets

• MNIST
• STL-10
• CIFAR-10
• CIFAR-100
• PASCAL VOC2007
• CelebA
• SVHN

Conventions

• Labels stored in an n x 1 tensor
• Images stored in an n x channels x height x width tensor