pytorch-image-models/search/search_index.json

{"config":{"lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Getting Started Install The library can be installed with pip: pip install timm Conda Environment All development and testing has been done in Conda Python 3 environments on Linux x86-64 systems, specifically Python 3.6.x, 3.7.x., 3.8.x. Little to no care has been taken to be Python 2.x friendly and will not support it. If you run into any challenges running on Windows, or other OS, I'm definitely open to looking into those issues so long as it's in a reproducible (read Conda) environment. PyTorch versions 1.4, 1.5.x, 1.6, and 1.7 have been tested with this code. I've tried to keep the dependencies minimal, the setup is as per the PyTorch default install instructions for Conda: conda create -n torch-env conda activate torch-env conda install -c pytorch pytorch torchvision cudatoolkit=11 conda install pyyaml Load a Pretrained Model Pretrained models can be loaded using timm.create_model import timm m = timm . create_model ( 'mobilenetv3_large_100' , pretrained = True ) m . eval () List Models with Pretrained Weights import timm from pprint import pprint model_names = timm . list_models ( pretrained = True ) pprint ( model_names ) >>> [ 'adv_inception_v3' , 'cspdarknet53' , 'cspresnext50' , 'densenet121' , 'densenet161' , 'densenet169' , 'densenet201' , 'densenetblur121d' , 'dla34' , 'dla46_c' , ... ] List Model Architectures by Wildcard import timm from pprint import pprint model_names = timm . list_models ( '*resne*t*' ) pprint ( model_names ) >>> [ 'cspresnet50' , 'cspresnet50d' , 'cspresnet50w' , 'cspresnext50' , ... ]","title":"Getting Started"},{"location":"#getting-started","text":"","title":"Getting Started"},{"location":"#install","text":"The library can be installed with pip: pip install timm Conda Environment All development and testing has been done in Conda Python 3 environments on Linux x86-64 systems, specifically Python 3.6.x, 3.7.x., 3.8.x. Little to no care has been taken to be Python 2.x friendly and will not support it. If you run into any challenges running on Windows, or other OS, I'm definitely open to looking into those issues so long as it's in a reproducible (read Conda) environment. PyTorch versions 1.4, 1.5.x, 1.6, and 1.7 have been tested with this code. I've tried to keep the dependencies minimal, the setup is as per the PyTorch default install instructions for Conda: conda create -n torch-env conda activate torch-env conda install -c pytorch pytorch torchvision cudatoolkit=11 conda install pyyaml","title":"Install"},{"location":"#load-a-pretrained-model","text":"Pretrained models can be loaded using timm.create_model import timm m = timm . create_model ( 'mobilenetv3_large_100' , pretrained = True ) m . eval ()","title":"Load a Pretrained Model"},{"location":"#list-models-with-pretrained-weights","text":"import timm from pprint import pprint model_names = timm . list_models ( pretrained = True ) pprint ( model_names ) >>> [ 'adv_inception_v3' , 'cspdarknet53' , 'cspresnext50' , 'densenet121' , 'densenet161' , 'densenet169' , 'densenet201' , 'densenetblur121d' , 'dla34' , 'dla46_c' , ... ]","title":"List Models with Pretrained Weights"},{"location":"#list-model-architectures-by-wildcard","text":"import timm from pprint import pprint model_names = timm . list_models ( '*resne*t*' ) pprint ( model_names ) >>> [ 'cspresnet50' , 'cspresnet50d' , 'cspresnet50w' , 'cspresnext50' , ... ]","title":"List Model Architectures by Wildcard"},{"location":"archived_changes/","text":"Archived Changes May 12, 2020 Add ResNeSt models (code adapted from https://github.com/zhanghang1989/ResNeSt , paper https://arxiv.org/abs/2004.08955 )) May 3, 2020 Pruned EfficientNet B1, B2, and B3 ( https://arxiv.org/abs/2002.08258 ) contributed by Yonathan Aflalo May 1, 2020 Merged a number of execellent contributions in the ResNet model family over the past month BlurPool2D and resnetblur models initiated by Chris Ha , I trained resnetblur50 to 79.3. TResNet models and SpaceToDepth, AntiAliasDownsamp