A simple framework to automate image classification. Simply create the ./image/train and ./image/test directories with labeled subdirectories, and then fill each subdirectory with representative images. Then run one of the two shell scripts below to begin training (or both).
$ mkdir -p images/train # Optional
$ mkdir -p images/test # Optional
$ python download_cifar10_images.py # Download cifar10 files
The container for this project is based on the nvidia/cuda docker implementation. Thus, prerequisites for running this container are equivalent to those at https://hub.docker.com/r/nvidia/cuda/ .
Note: The container should still run with only a CPU (i.e. no GPU), albeit slowly.
$ ./inception.sh
$ ./autokeras.sh