timm (pytorch-image-models) is an image model library based on PyTorch. It provides a large number of pre-trained models and training scripts, covering a wide range of tasks such as image classification, object detection, and image segmentation.
timm provides a variety of image model implementations, including classic AlexNet, VGG, ResNet, Inception, DenseNet, EfficientNet, etc., as well as some latest models, such as RegNet, RepVGG, Swin Transformer, etc. These models have achieved state-of-the-art performance on several image classification benchmark datasets with good scalability and ease of use.
In addition to providing pre-trained models, timm also provides some training scripts to help users quickly build their own training process. At the same time, timm also provides some auxiliary functions and tools, such as data enhancement, learning rate scheduler, model visualization, etc., to facilitate users to customize and optimize the model.
In short, timm is a very practical and powerful image model library, which can help users quickly build efficient image models, and also facilitates model adjustment and optimization.
timm - transfer learning model library under pytorch Detailed tutorial