Getting Started with PyTorch Image Models (timm): a practitioner's guide

PyTorch Image Models (timm) is a library for state-of-the-art image classification, containing a collection of image models, optimizers, schedulers, augmentations and much more; it was recently named the top trending library on papers-with-code of 2021! Whilst there are an increasing number of low and no code solutions which make it easy to get started with applying Deep Learning to computer vision problems, in my current role as part of Microsoft CSE, we frequently engage with customers who wish to pursue custom solutions tailored to their specific problem; utilizing the latest and greatest innovations to exceed the performance level offered by these services. Due to the rate that new architectures and training techniques are introduced into this rapidly moving field, whether you are a beginner or an expert, it can be difficult to keep up with the latest practices and make it challenging to know where to start when approaching new vision tasks with the intention of reproducing similar results to those presented in academic benchmarks. Whether I'm training from scratch or finetuning existing models to new tasks, and looking to leverage pre-existing components to speed up my workflow, timm is one of my favourite libraries for computer vision in PyTorch. However, whilst timm contains reference training and validation scripts for reproducing ImageNet training results and has documentation covering the core components in the official documentation and the timmdocs project, due to the sheer number of features that the library provides it can be difficult to know where to get started when applying these in custom use-cases. The purpose of this guide is to explore timm from a practitioner's point of view, focusing on how to use some of the features and components included in timm in custom training scripts. The focus is not to explore how or why these concepts work, or how they are implemented in timm; for this, links to the original papers will be provided where appropriate, and I would recommend timmdocs to learn more about timm's internals. Additionally, this article is by no means exhaustive, the areas selected are based upon my personal experience using this library. All information here is based on timm 0.5.4 which was recently released at the time of writing. Whilst this article can be read in order, it may also be useful as a reference for a particular part of the library. For ease of navigation, a table of contents is presented below. Tl;dr: If you just want to see some working code that you can use directly, all of the code required to replicate this post is available as a GitHub gist here. One of the most popular features of timm is its large, and ever-growing collection of model architectures.