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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
huggingface pytorch-image-models - GitHub 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
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
timm - Hugging Face We’re on a journey to advance and democratize artificial intelligence through open source and open science
Using timm at Hugging Face timm, also known as pytorch-image-models, is an open-source collection of state-of-the-art PyTorch image models, pretrained weights, and utility scripts for training, inference, and validation
Installation - Hugging Face Before you start, you’ll need to setup your environment and install the appropriate packages timm is tested on Python 3+ You should install timm in a virtual environment to keep things tidy and avoid dependency conflicts
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