.. _models: Models and pre-trained weights ############################## 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. General information on pre-trained weights ========================================== TorchVision offers pre-trained weights for every provided architecture, using the PyTorch :mod:`torch.hub`. Instancing a pre-trained model will download its weights to a cache directory. This directory can be set using the `TORCH_HOME` environment variable. See :func:`torch.hub.load_state_dict_from_url` for details. .. note:: The pre-trained models provided in this library may have their own licenses or terms and conditions derived from the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case. .. 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`` (serialized using older versions of PyTorch) may not preserve the historic behaviour. Refer to the following `documentation `_ Initializing pre-trained models ------------------------------- As of v0.13, TorchVision offers a new `Multi-weight support API `_ for loading different weights to the existing model builder methods: .. code:: python from torchvision.models import resnet50, ResNet50_Weights # Old weights with accuracy 76.130% resnet50(weights=ResNet50_Weights.IMAGENET1K_V1) # New weights with accuracy 80.858% resnet50(weights=ResNet50_Weights.IMAGENET1K_V2) # Best available weights (currently alias for IMAGENET1K_V2) # Note that these weights may change across versions resnet50(weights=ResNet50_Weights.DEFAULT) # Strings are also supported resnet50(weights="IMAGENET1K_V2") # No weights - random initialization resnet50(weights=None) Migrating to the new API is very straightforward. The following method calls between the 2 APIs are all equivalent: .. code:: python from torchvision.models import resnet50, ResNet50_Weights # Using pretrained weights: resnet50(weights=ResNet50_Weights.IMAGENET1K_V1) resnet50(weights="IMAGENET1K_V1") resnet50(pretrained=True) # deprecated resnet50(True) # deprecated # Using no weights: resnet50(weights=None) resnet50() resnet50(pretrained=False) # deprecated resnet50(False) # deprecated Note that the ``pretrained`` parameter is now deprecated, using it will emit warnings and will be removed on v0.15. Using the pre-trained models ---------------------------- Before using the pre-trained models, one must preprocess the image (resize with right resolution/interpolation, apply inference transforms, rescale the values etc). There is no standard way to do this as it depends on how a given model was trained. It can vary across model families, variants or even weight versions. Using the correct preprocessing method is critical and failing to do so may lead to decreased accuracy or incorrect outputs. All the necessary information for the inference transforms of each pre-trained model is provided on its weights documentation. To simplify inference, TorchVision bundles the necessary preprocessing transforms into each model weight. These are accessible via the ``weight.transforms`` attribute: .. code:: python # Initialize the Weight Transforms weights = ResNet50_Weights.DEFAULT preprocess = weights.transforms() # Apply it to the input image img_transformed = preprocess(img) Some models use modules which have different training and evaluation behavior, such as batch normalization. To switch between these modes, use ``model.train()`` or ``model.eval()`` as appropriate. See :meth:`~torch.nn.Module.train` or :meth:`~torch.nn.Module.eval` for details. .. code:: python # Initialize model weights = ResNet50_Weights.DEFAULT model = resnet50(weights=weights) # Set model to eval mode model.eval() Listing and retrieving available models --------------------------------------- As of v0.14, TorchVision offers a new mechanism which allows listing and retrieving models and weights by their names. Here are a few examples on how to use them: .. code:: python # List available models all_models = list_models() classification_models = list_models(module=torchvision.models) # Initialize models m1 = get_model("mobilenet_v3_large", weights=None) m2 = get_model("quantized_mobilenet_v3_large", weights="DEFAULT") # Fetch weights weights = get_weight("MobileNet_V3_Large_QuantizedWeights.DEFAULT") assert weights == MobileNet_V3_Large_QuantizedWeights.DEFAULT weights_enum = get_model_weights("quantized_mobilenet_v3_large") assert weights_enum == MobileNet_V3_Large_QuantizedWeights weights_enum2 = get_model_weights(torchvision.models.quantization.mobilenet_v3_large) assert weights_enum == weights_enum2 Here are the available public functions to retrieve models and their corresponding weights: .. currentmodule:: torchvision.models .. autosummary:: :toctree: generated/ :template: function.rst get_model get_model_weights get_weight list_models Using models from Hub --------------------- Most pre-trained models can be accessed directly via PyTorch Hub without having TorchVision installed: .. code:: python import torch # Option 1: passing weights param as string model = torch.hub.load("pytorch/vision", "resnet50", weights="IMAGENET1K_V2") # Option 2: passing weights param as enum weights = torch.hub.load("pytorch/vision", "get_weight", weights="ResNet50_Weights.IMAGENET1K_V2") model = torch.hub.load("pytorch/vision", "resnet50", weights=weights) You can also retrieve all the available weights of a specific model via PyTorch Hub by doing: .. code:: python import torch weight_enum = torch.hub.load("pytorch/vision", "get_model_weights", name="resnet50") print([weight for weight in weight_enum]) The only exception to the above are the detection models included on :mod:`torchvision.models.detection`. These models require TorchVision to be installed because they depend on custom C++ operators. Classification ============== .. currentmodule:: torchvision.models The following classification models are available, with or without pre-trained weights: .. toctree:: :maxdepth: 1 models/alexnet models/convnext models/densenet models/efficientnet models/efficientnetv2 models/googlenet models/inception models/maxvit models/mnasnet models/mobilenetv2 models/mobilenetv3 models/regnet models/resnet models/resnext models/shufflenetv2 models/squeezenet models/swin_transformer models/vgg models/vision_transformer models/wide_resnet | Here is an example of how to use the pre-trained image classification models: .. code:: python from torchvision.io import read_image from torchvision.models import resnet50, ResNet50_Weights img = read_image("test/assets/encode_jpeg/grace_hopper_517x606.jpg") # Step 1: Initialize model with the best available weights weights = ResNet50_Weights.DEFAULT model = resnet50(weights=weights) model.eval() # Step 2: Initialize the inference transforms preprocess = weights.transforms() # Step 3: Apply inference preprocessing transforms batch = preprocess(img).unsqueeze(0) # Step 4: Use the model and print the predicted category prediction = model(batch).squeeze(0).softmax(0) class_id = prediction.argmax().item() score = prediction[class_id].item() category_name = weights.meta["categories"][class_id] print(f"{category_name}: {100 * score:.1f}%") The classes of the pre-trained model outputs can be found at ``weights.meta["categories"]``. Table of all available classification weights --------------------------------------------- Accuracies are reported on ImageNet-1K using single crops: .. include:: generated/classification_table.rst Quantized models ---------------- .. currentmodule:: torchvision.models.quantization The following architectures provide support for INT8 quantized models, with or without pre-trained weights: .. toctree:: :maxdepth: 1 models/googlenet_quant models/inception_quant models/mobilenetv2_quant models/mobilenetv3_quant models/resnet_quant models/resnext_quant models/shufflenetv2_quant | Here is an example of how to use the pre-trained quantized image classification models: .. code:: python from torchvision.io import read_image from torchvision.models.quantization import resnet50, ResNet50_QuantizedWeights img = read_image("test/assets/encode_jpeg/grace_hopper_517x606.jpg") # Step 1: Initialize model with the best available weights weights = ResNet50_QuantizedWeights.DEFAULT model = resnet50(weights=weights, quantize=True) model.eval() # Step 2: Initialize the inference transforms preprocess = weights.transforms() # Step 3: Apply inference preprocessing transforms batch = preprocess(img).unsqueeze(0) # Step 4: Use the model and print the predicted category prediction = model(batch).squeeze(0).softmax(0) class_id = prediction.argmax().item() score = prediction[class_id].item() category_name = weights.meta["categories"][class_id] print(f"{category_name}: {100 * score}%") The classes of the pre-trained model outputs can be found at ``weights.meta["categories"]``. Table of all available quantized classification weights ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Accuracies are reported on ImageNet-1K using single crops: .. include:: generated/classification_quant_table.rst Semantic Segmentation ===================== .. currentmodule:: torchvision.models.segmentation .. betastatus:: segmentation module The following semantic segmentation models are available, with or without pre-trained weights: .. toctree:: :maxdepth: 1 models/deeplabv3 models/fcn models/lraspp | Here is an example of how to use the pre-trained semantic segmentation models: .. code:: python from torchvision.io.image import read_image from torchvision.models.segmentation import fcn_resnet50, FCN_ResNet50_Weights from torchvision.transforms.functional import to_pil_image img = read_image("gallery/assets/dog1.jpg") # Step 1: Initialize model with the best available weights weights = FCN_ResNet50_Weights.DEFAULT model = fcn_resnet50(weights=weights) model.eval() # Step 2: Initialize the inference transforms preprocess = weights.transforms() # Step 3: Apply inference preprocessing transforms batch = preprocess(img).unsqueeze(0) # Step 4: Use the model and visualize the prediction prediction = model(batch)["out"] normalized_masks = prediction.softmax(dim=1) class_to_idx = {cls: idx for (idx, cls) in enumerate(weights.meta["categories"])} mask = normalized_masks[0, class_to_idx["dog"]] to_pil_image(mask).show() The classes of the pre-trained model outputs can be found at ``weights.meta["categories"]``. The output format of the models is illustrated in :ref:`semantic_seg_output`. Table of all available semantic segmentation weights ---------------------------------------------------- All models are evaluated a subset of COCO val2017, on the 20 categories that are present in the Pascal VOC dataset: .. include:: generated/segmentation_table.rst .. _object_det_inst_seg_pers_keypoint_det: Object Detection, Instance Segmentation and Person Keypoint Detection ===================================================================== The pre-trained models for detection, instance segmentation and keypoint detection are initialized with the classification models in torchvision. The models expect a list of ``Tensor[C, H, W]``. Check the constructor of the models for more information. .. betastatus:: detection module Object Detection ---------------- .. currentmodule:: torchvision.models.detection The following object detection models are available, with or without pre-trained weights: .. toctree:: :maxdepth: 1 models/faster_rcnn models/fcos models/retinanet models/ssd models/ssdlite | Here is an example of how to use the pre-trained object detection models: .. code:: python from torchvision.io.image import read_image from torchvision.models.detection import fasterrcnn_resnet50_fpn_v2, FasterRCNN_ResNet50_FPN_V2_Weights from torchvision.utils import draw_bounding_boxes from torchvision.transforms.functional import to_pil_image img = read_image("test/assets/encode_jpeg/grace_hopper_517x606.jpg") # Step 1: Initialize model with the best available weights weights = FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULT model = fasterrcnn_resnet50_fpn_v2(weights=weights, box_score_thresh=0.9) model.eval() # Step 2: Initialize the inference transforms preprocess = weights.transforms() # Step 3: Apply inference preprocessing transforms batch = [preprocess(img)] # Step 4: Use the model and visualize the prediction prediction = model(batch)[0] labels = [weights.meta["categories"][i] for i in prediction["labels"]] box = draw_bounding_boxes(img, boxes=prediction["boxes"], labels=labels, colors="red", width=4, font_size=30) im = to_pil_image(box.detach()) im.show() The classes of the pre-trained model outputs can be found at ``weights.meta["categories"]``. For details on how to plot the bounding boxes of the models, you may refer to :ref:`instance_seg_output`. Table of all available Object detection weights ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Box MAPs are reported on COCO val2017: .. include:: generated/detection_table.rst Instance Segmentation --------------------- .. currentmodule:: torchvision.models.detection The following instance segmentation models are available, with or without pre-trained weights: .. toctree:: :maxdepth: 1 models/mask_rcnn | For details on how to plot the masks of the models, you may refer to :ref:`instance_seg_output`. Table of all available Instance segmentation weights ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Box and Mask MAPs are reported on COCO val2017: .. include:: generated/instance_segmentation_table.rst Keypoint Detection ------------------ .. currentmodule:: torchvision.models.detection The following person keypoint detection models are available, with or without pre-trained weights: .. toctree:: :maxdepth: 1 models/keypoint_rcnn | The classes of the pre-trained model outputs can be found at ``weights.meta["keypoint_names"]``. For details on how to plot the bounding boxes of the models, you may refer to :ref:`keypoint_output`. Table of all available Keypoint detection weights ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Box and Keypoint MAPs are reported on COCO val2017: .. include:: generated/detection_keypoint_table.rst Video Classification ==================== .. currentmodule:: torchvision.models.video .. betastatus:: video module The following video classification models are available, with or without pre-trained weights: .. toctree:: :maxdepth: 1 models/video_mvit models/video_resnet models/video_s3d models/video_swin_transformer | Here is an example of how to use the pre-trained video classification models: .. code:: python from torchvision.io.video import read_video from torchvision.models.video import r3d_18, R3D_18_Weights vid, _, _ = read_video("test/assets/videos/v_SoccerJuggling_g23_c01.avi", output_format="TCHW") vid = vid[:32] # optionally shorten duration # Step 1: Initialize model with the best available weights weights = R3D_18_Weights.DEFAULT model = r3d_18(weights=weights) model.eval() # Step 2: Initialize the inference transforms preprocess = weights.transforms() # Step 3: Apply inference preprocessing transforms batch = preprocess(vid).unsqueeze(0) # Step 4: Use the model and print the predicted category prediction = model(batch).squeeze(0).softmax(0) label = prediction.argmax().item() score = prediction[label].item() category_name = weights.meta["categories"][label] print(f"{category_name}: {100 * score}%") The classes of the pre-trained model outputs can be found at ``weights.meta["categories"]``. Table of all available video classification weights --------------------------------------------------- Accuracies are reported on Kinetics-400 using single crops for clip length 16: .. include:: generated/video_table.rst Optical Flow ============ .. currentmodule:: torchvision.models.optical_flow The following Optical Flow models are available, with or without pre-trained .. toctree:: :maxdepth: 1 models/raft