- timm (PyTorch Image Models) - Hugging Face
Py T orch Im age M odels (timm) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders augmentations, and reference training validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results
- GitHub - huggingface pytorch-image-models: The largest . . .
Py T orch Im age M odels (timm) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders augmentations, and reference training validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results
- timm·PyPI
Py T orch Im age M odels (timm) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders augmentations, and reference training validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results
- Pytorch Image Models (timm) | timmdocs
`timm` is a deep-learning library created by Ross Wightman and is a collection of SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations and also training validating scripts with ability to reproduce ImageNet training results
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- timm - Hugging Face
timm is a library containing SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations, and training evaluation scripts It comes packaged with >700 pretrained models, and is designed to be flexible and easy to use Read the quick start guide to get up and running with the timm library
- pytorch-image-models timm models vision_transformer. py at . . .
The largest collection of PyTorch image encoders backbones Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V
- How to train your own models using timm? | timmdocs - fast
In this tutorial we will be only be looking at the above 7 features and look at how you could utilize timm to use these features for your own experiments on a custom dataset
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