# Deep residual networks pre-trained on ImageNet
model = torch.hub.load('pytorch/vision:v0.9.0', 'resnet18', pretrained=True)
# or any of these variants
# model = torch.hub.load('pytorch/vision:v0.9.0', 'resnet34', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.9.0', 'resnet50', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.9.0', 'resnet101', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.9.0', 'resnet152', pretrained=True)
url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
try: urllib.URLopener().retrieve(url, filename)
except: urllib.request.urlretrieve(url, filename)
from torchvision import transforms
input_image = Image.open(filename)
preprocess = transforms.Compose([
transforms.CenterCrop(224),
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')
output = model(input_batch)
# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
probabilities = torch.nn.functional.softmax(output[0], dim=0)
# Download ImageNet labels
!wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt
with open("imagenet_classes.txt", "r") as f:
categories = [s.strip() for s in f.readlines()]
# Show top categories per image
top5_prob, top5_catid = torch.topk(probabilities, 5)
for i in range(top5_prob.size(0)):
print(categories[top5_catid[i]], top5_prob[i].item())
#Model structure Top-1 error Top-5 error