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 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 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 train() or eval() for details.
# Initialize model weights = ResNet50_Weights.DEFAULT model = resnet50(weights=weights) # Set model to eval mode model.eval() Model Registration Mechanism¶
Warning
The registration mechanism is in Beta stage, and backward compatibility is not guaranteed.
As of v0.14, TorchVision offers a new model registration mechanism which allows retreaving 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 methods of the model registration mechanism:
| Gets the model name and configuration and returns an instantiated model. |
| Retuns the weights enum class associated to the given model. |
| Gets the weights enum value by its full name. |
| Returns a list with the names of registered 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 torchvision.models.detection. These models require TorchVision to be installed because they depend on custom C++ operators.
Classification¶
The following classification models are available, with or without pre-trained weights:
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:
Weight | Acc@1 | Acc@5 | Params | Recipe |
|---|---|---|---|---|
56.522 | 79.066 | 61.1M | ||
84.062 | 96.87 | 88.6M | ||
84.414 | 96.976 | 197.8M | ||
83.616 | 96.65 | 50.2M | ||
82.52 | 96.146 | 28.6M | ||
74.434 | 91.972 | 8.0M | ||
77.138 | 93.56 | 28.7M | ||
75.6 | 92.806 | 14.1M | ||
76.896 | 93.37 | 20.0M | ||
77.692 | 93.532 | 5.3M | ||
78.642 | 94.186 | 7.8M | ||
79.838 | 94.934 | 7.8M | ||
80.608 | 95.31 | 9.1M | ||
82.008 | 96.054 | 12.2M | ||
83.384 | 96.594 | 19.3M | ||
83.444 | 96.628 | 30.4M | ||
84.008 | 96.916 | 43.0M | ||
84.122 | 96.908 | 66.3M | ||
85.808 | 97.788 | 118.5M | ||
85.112 | 97.156 | 54.1M | ||
84.228 | 96.878 | 21.5M | ||
69.778 | 89.53 | 6.6M | ||
77.294 | 93.45 | 27.2M | ||
67.734 | 87.49 | 2.2M | ||
71.18 | 90.496 | 3.2M | ||
73.456 | 91.51 | 4.4M | ||
76.506 | 93.522 | 6.3M | ||
83.7 | 96.722 | 30.9M | ||
71.878 | 90.286 | 3.5M | ||
72.154 | 90.822 | 3.5M | ||
74.042 | 91.34 | 5.5M | ||
75.274 | 92.566 | 5.5M | ||
67.668 | 87.402 | 2.5M | ||
80.058 | 94.944 | 54.3M | ||
82.716 | 96.196 | 54.3M | ||
77.04 | 93.44 | 9.2M | ||
79.668 | 94.922 | 9.2M | ||
80.622 | 95.248 | 107.8M | ||
83.014 | 96.288 | 107.8M | ||
78.364 | 93.992 | 15.3M | ||
81.196 | 95.43 | 15.3M | ||
72.834 | 90.95 | 5.5M | ||
74.864 | 92.322 | 5.5M | ||
75.212 | 92.348 | 7.3M | ||
77.522 | 93.826 | 7.3M | ||
79.344 | 94.686 | 39.6M | ||
81.682 | 95.678 | 39.6M | ||
88.228 | 98.682 | 644.8M | ||
86.068 | 97.844 | 644.8M | ||
80.424 | 95.24 | 83.6M | ||
82.886 | 96.328 | 83.6M | ||
86.012 | 98.054 | 83.6M | ||
83.976 | 97.244 | 83.6M | ||
77.95 | 93.966 | 11.2M | ||
80.876 | 95.444 | 11.2M | ||
80.878 | 95.34 | 145.0M | ||
83.368 | 96.498 | 145.0M | ||
86.838 | 98.362 | 145.0M | ||
84.622 | 97.48 | 145.0M | ||
78.948 | 94.576 | 19.4M | ||
81.982 | 95.972 | 19.4M | ||
74.046 | 91.716 | 4.3M | ||
75.804 | 92.742 | 4.3M | ||
76.42 | 93.136 | 6.4M | ||
78.828 | 94.502 | 6.4M | ||
80.032 | 95.048 | 39.4M | ||
82.828 | 96.33 | 39.4M | ||
79.312 | 94.526 | 88.8M | ||
82.834 | 96.228 | 88.8M | ||
83.246 | 96.454 | 83.5M | ||
77.618 | 93.698 | 25.0M | ||
81.198 | 95.34 | 25.0M | ||
77.374 | 93.546 | 44.5M | ||
81.886 | 95.78 | 44.5M | ||
78.312 | 94.046 | 60.2M | ||
82.284 | 96.002 | 60.2M | ||
69.758 | 89.078 | 11.7M | ||
73.314 | 91.42 | 21.8M | ||
76.13 | 92.862 | 25.6M | ||
80.858 | 95.434 | 25.6M | ||
60.552 | 81.746 | 1.4M | ||
69.362 | 88.316 | 2.3M | ||
72.996 | 91.086 | 3.5M | ||
76.23 | 93.006 | 7.4M | ||
58.092 | 80.42 | 1.2M | ||
58.178 | 80.624 | 1.2M | ||
83.582 | 96.64 | 87.8M | ||
83.196 | 96.36 | 49.6M | ||
81.474 | 95.776 | 28.3M | ||
84.112 | 96.864 | 87.9M | ||
83.712 | 96.816 | 49.7M | ||
82.072 | 96.132 | 28.4M | ||
70.37 | 89.81 | 132.9M | ||
69.02 | 88.628 | 132.9M | ||
71.586 | 90.374 | 133.1M | ||
69.928 | 89.246 | 133.0M | ||
73.36 | 91.516 | 138.4M | ||
71.592 | 90.382 | 138.4M | ||
nan | nan | 138.4M | ||
74.218 | 91.842 | 143.7M | ||
72.376 | 90.876 | 143.7M | ||
81.072 | 95.318 | 86.6M | ||
85.304 | 97.65 | 86.9M | ||
81.886 | 96.18 | 86.6M | ||
75.912 | 92.466 | 88.2M | ||
88.552 | 98.694 | 633.5M | ||
85.708 | 97.73 | 632.0M | ||
79.662 | 94.638 | 304.3M | ||
88.064 | 98.512 | 305.2M | ||
85.146 | 97.422 | 304.3M | ||
76.972 | 93.07 | 306.5M | ||
78.848 | 94.284 | 126.9M | ||
82.51 | 96.02 | 126.9M | ||
78.468 | 94.086 | 68.9M | ||
81.602 | 95.758 | 68.9M |
Quantized models¶
The following architectures provide support for INT8 quantized models, with or without pre-trained weights:
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:
Weight | Acc@1 | Acc@5 | Params | Recipe |
|---|---|---|---|---|
69.826 | 89.404 | 6.6M | ||
77.176 | 93.354 | 27.2M | ||
71.658 | 90.15 | 3.5M | ||
73.004 | 90.858 | 5.5M | ||
78.986 | 94.48 | 88.8M | ||
82.574 | 96.132 | 88.8M | ||
82.898 | 96.326 | 83.5M | ||
69.494 | 88.882 | 11.7M | ||
75.92 | 92.814 | 25.6M | ||
80.282 | 94.976 | 25.6M | ||
57.972 | 79.78 | 1.4M | ||
68.36 | 87.582 | 2.3M | ||
72.052 | 90.7 | 3.5M | ||
75.354 | 92.488 | 7.4M |
Semantic Segmentation¶
Warning
The segmentation module is in Beta stage, and backward compatibility is not guaranteed.
The following semantic segmentation models are available, with or without pre-trained weights:
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 Semantic segmentation models.
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:
Weight | Mean IoU | pixelwise Acc | Params | Recipe |
|---|---|---|---|---|
| 60.3 | 91.2 | 11.0M | |
67.4 | 92.4 | 61.0M | ||
66.4 | 92.4 | 42.0M | ||
63.7 | 91.9 | 54.3M | ||
60.5 | 91.4 | 35.3M | ||
57.9 | 91.2 | 3.2M |
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.
Warning
The detection module is in Beta stage, and backward compatibility is not guaranteed.
Object Detection¶
The following object detection models are available, with or without pre-trained weights:
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 Instance segmentation models.
Table of all available Object detection weights¶
Box MAPs are reported on COCO val2017:
Weight | Box MAP | Params | Recipe |
|---|---|---|---|
39.2 | 32.3M | ||
22.8 | 19.4M | ||
32.8 | 19.4M | ||
46.7 | 43.7M | ||
37 | 41.8M | ||
41.5 | 38.2M | ||
36.4 | 34.0M | ||
25.1 | 35.6M | ||
21.3 | 3.4M |
Instance Segmentation¶
The following instance segmentation models are available, with or without pre-trained weights:
For details on how to plot the masks of the models, you may refer to Instance segmentation models.
Table of all available Instance segmentation weights¶
Box and Mask MAPs are reported on COCO val2017:
Weight | Box MAP | Mask MAP | Params | Recipe |
|---|---|---|---|---|
47.4 | 41.8 | 46.4M | ||
37.9 | 34.6 | 44.4M |
Keypoint Detection¶
The following person keypoint detection models are available, with or without pre-trained weights:
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 Visualizing keypoints.
Table of all available Keypoint detection weights¶
Box and Keypoint MAPs are reported on COCO val2017:
Weight | Box MAP | Keypoint MAP | Params | Recipe |
|---|---|---|---|---|
50.6 | 61.1 | 59.1M | ||
54.6 | 65 | 59.1M |
Video Classification¶
Warning
The video module is in Beta stage, and backward compatibility is not guaranteed.
The following video classification models are available, with or without pre-trained weights:
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:
Weight | Acc@1 | Acc@5 | Params | Recipe |
|---|---|---|---|---|
63.96 | 84.13 | 11.7M | ||
78.477 | 93.582 | 36.6M | ||
80.757 | 94.665 | 34.5M | ||
67.463 | 86.175 | 31.5M | ||
63.2 | 83.479 | 33.4M | ||
68.368 | 88.05 | 8.3M |
Optical Flow¶
The following Optical Flow models are available, with or without pre-trained
