Update README.md

Author: y0ast

README.md

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 This is a collection of PyTorch snippets that have helped me (and others) significantly with doing ML research.
 
-* A minimal [CIFAR-10 training script](minimal_cifar) to get 94% in ~150 lines, for a simple but strong baseline.
-* [FastMNIST](fast_mnist): a drop-in replacement for the standard MNIST dataset which speeds up training on the **GPU** (2x !) by avoiding unnecessary preprocessing.
+* [Minimal CIFAR-10](minimal_cifar) A very simple training script to get 94% accuracy with just ~150 lines of code, for an easy but strong baseline.
+* [FastMNIST](fast_mnist): a drop-in replacement for the standard MNIST dataset which speeds up training on the **GPU** (by 2x !) by avoiding unnecessary preprocessing that pegs the cpu at 100% for small models.
 * [Subset of ImageNet](subset_of_imagenet): it's remarkably difficult to train on a subset of the ImageNet classes with the default TorchVision datasets. This snippet makes the minimal changes to them to make it very easy.
+* [ImageNet dogs vs not dogs](imagenet_dogs_vs_notdogs): a standardized setup for the ImageNet Dogs vs Not Dogs out-of-distribution detection task.
 
-In a [separate repository](https://github.com/y0ast/slurm-for-ml) I explain how I use `slurm` job arrays without pain using a simple 1 file shell script.
+In a separate repository, [Slurm for ML](https://github.com/y0ast/slurm-for-ml), I explain how I use `slurm` job arrays without pain using a simple 1 file shell script.