.. _models_new: Models and pre-trained weights - New #################################### .. note:: These are the new models docs, documenting the new multi-weight API. TODO: Once all is done, remove the "- New" part in the title above, and rename this file as models.rst The ``torchvision.models`` subpackage contains definitions of models for addressing different tasks, including: image classification, pixelwise semantic segmentation, object detection, instance segmentation, person keypoint detection, video classification, and optical flow. .. note :: Backward compatibility is guaranteed for loading a serialized ``state_dict`` to the model created using old PyTorch version. On the contrary, loading entire saved models or serialized ``ScriptModules`` (seralized using older versions of PyTorch) may not preserve the historic behaviour. Refer to the following `documentation `_ Classification ============== .. currentmodule:: torchvision.models The following classification models are available, with or without pre-trained weights: .. toctree:: :maxdepth: 1 models/convnext models/densenet models/efficientnet models/efficientnetv2 models/googlenet models/regnet models/resnet models/resnext models/squeezenet models/vgg models/vision_transformer Table of all available classification weights --------------------------------------------- Accuracies are reported on ImageNet .. include:: generated/classification_table.rst Object Detection, Instance Segmentation and Person Keypoint Detection ===================================================================== .. currentmodule:: torchvision.models.detection The following detection models are available, with or without pre-trained weights: .. toctree:: :maxdepth: 1 models/retinanet Table of all available detection weights ---------------------------------------- Box MAPs are reported on COCO .. include:: generated/detection_table.rst