PyTorch Image Model Framework

Learn the fundamental concepts of the PyTorch Image Model framework.


Nowadays, developers use PyTorch or Tensorflow frameworks to train image classification models. This course focuses on PyTorch implementation via an open-source Python library called the PyTorch Image Model.

The PyTorch Image Model (timm) is a deep-learning framework created by Ross Wightman. It contains models, utilities, data loaders, and scripts related to computer vision. Most of the base implementation relies on the state-of-the-art algorithms adapted for PyTorch.

The main objective of this framework is to provide an out-of-the-box library for researchers and developers to reproduce training results for ImageNet.

It’s in active development and is suitable for testing various state-of-the-art architectures for image classification.

The PyTorch Image Model framework offers a lot of possible architectures with the option to change it with a single line of code. Some of the most popular architectures include:

  • MixNet-XL
  • SE-ResNeXt-26-D
  • EfficientNet-B0
  • EfficientNet-B2
  • EfficientNet-B3
  • EfficientNet-ES
  • ResNet50
  • MobileNetV3-Large-100

In this course, we’ll focus on the usage of the following architectures:

  • ResNet50
  • EfficientNet-B0


The PyTorch Image Model also provides its own validation results. Developers can cross-check the output and benchmark the performance of their desired models.

Based on its latest benchmark, ResNet50 achieved 79.038 in top-1 accuracy. The top-5 accuracy was 94.390.



The code provided by this framework is based on the Apache 2.0 license. We can train custom classification models with it for commercial purposes.

Pre-trained weights (ImageNet)

Most of the pre-trained weights provided by this framework were trained using ImageNet datasets, ImageNet was released for noncommercial purposes. If we intend to use the pre-trained weights directly, we have to seek legal advice on it.

Pre-trained weights (Others)

Pre-trained weights such as Facebook’s WSL, SSL, SWSL ResNe(Xt), and the Google Noisy Student EfficientNet were trained using proprietary datasets with an explicit noncommercial license (CC-BY-NC 4.0).


We can find the official documentation at Pytorch Image Model. There’s also alternative comprehensive documentation called timmdocs.

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