import torch
model = torch.hub.load('pytorch/vision:v0.4.2', 'vgg11', pretrained=True)
# or any of these variants
# model = torch.hub.load('pytorch/vision:v0.4.2', 'vgg11_bn', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.4.2', 'vgg13', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.4.2', 'vgg13_bn', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.4.2', 'vgg16', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.4.2', 'vgg16_bn', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.4.2', 'vgg19', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.4.2', 'vgg19_bn', pretrained=True)
model.eval()

All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].

Here’s a sample execution.

# Download an example image from the pytorch website
import urllib
url, filename = ("https://github.com/pytorch/hub/raw/master/dog.jpg", "dog.jpg")
try: urllib.URLopener().retrieve(url, filename)
except: urllib.request.urlretrieve(url, filename)
# sample execution (requires torchvision)
from PIL import Image
from torchvision import transforms
input_image = Image.open(filename)
preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model

# move the input and model to GPU for speed if available
if torch.cuda.is_available():
    input_batch = input_batch.to('cuda')
    model.to('cuda')

with torch.no_grad():
    output = model(input_batch)
# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
print(output[0])
# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
print(torch.nn.functional.softmax(output[0], dim=0))

Model Description

Here we have implementations for the models proposed in Very Deep Convolutional Networks for Large-Scale Image Recognition, for each configurations and their with bachnorm version.

For example, configuration A presented in the paper is vgg11, configuration B is vgg13, configuration D is vgg16 and configuration E is vgg19. Their batchnorm version are suffixed with _bn.

Their 1-crop error rates on imagenet dataset with pretrained models are listed below.

Model structure Top-1 error Top-5 error
vgg11 30.98 11.37
vgg11_bn 26.70 8.58
vgg13 30.07 10.75
vgg13_bn 28.45 9.63
vgg16 28.41 9.62
vgg16_bn 26.63 8.50
vgg19 27.62 9.12
vgg19_bn 25.76 8.15

References