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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:

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:

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:

# 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.

# 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:

# 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:

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:

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:

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:

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:

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:

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:

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:

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:

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:

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:

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:

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:

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:

Optical Flow

.. currentmodule:: torchvision.models.optical_flow

The following Optical Flow models are available, with or without pre-trained

.. toctree::
   :maxdepth: 1

   models/raft