resnet pytorch

ResNet (Residual Neural Network) is a popular deep learning architecture commonly used for image classification tasks. It was introduced by Kaiming He et al. in 2015. ResNet uses residual connections to allow the network to learn residual mappings, making it easier to train very deep networks.

To implement ResNet in PyTorch, you can use the torchvision library, which provides pre-trained ResNet models as well as the ability to create custom ResNet architectures.

Here is an example of how to use the torchvision library to load a pre-trained ResNet model and perform image classification:

import torch
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image

# Load the pre-trained ResNet model
resnet = models.resnet50(pretrained=True)

# Preprocess the input image
transform = 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])
])

# Load and preprocess the image
image = Image.open("image.jpg")
input_tensor = transform(image)
input_batch = input_tensor.unsqueeze(0)

# Move the input tensor to the GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
input_batch = input_batch.to(device)

# Set the model to evaluation mode
resnet.eval()

# Make predictions
with torch.no_grad():
    input_batch = input_batch.to(device)
    output = resnet(input_batch)

# Load the class labels
with open("imagenet_classes.txt") as f:
    class_labels = [line.strip() for line in f.readlines()]

# Get the predicted class label
_, predicted_idx = torch.max(output, 1)
predicted_label = class_labels[predicted_idx.item()]

# Print the predicted label
print("Predicted label: ", predicted_label)

In the above code, the input image is preprocessed using the same transformations used during training of the ResNet model. The pre-trained ResNet model is loaded, and the input image is passed through the model to get predictions. The predicted class label is then obtained and printed.

Note: Make sure to replace "image.jpg" with the path to your input image and "imagenet_classes.txt" with the path to the class labels file corresponding to the pre-trained ResNet model you are using.