Today, a new library for performing image classification has made its debut:
The library is based on the PyTorch example code for imagenet. For ResNet-based networks, you can finetune pretrained models on your own data rather than just using the imagenet dataset. In addition, you can make predictions (single and batch/continuous), output information on built models and export trained models to TorchScript.
The library is also available via Docker images, one for GPU-based machines and one for CPU-only ones. However, the latter one should only be used for inference and not training, as it is simply too slow.
More information on the library and the Docker images is available from Github: