ResNet18 - PyTorch

Before jumping into the context, what is ResNet-18?

ResNet-18 is a convolutional neural network (CNN) that is 18 layers deep. It was first introduced in the paper "Deep Residual Learning for Image Recognition" by He et al. (2016). ResNet-18 is a popular CNN architecture for image classification tasks, and it is also used for other computer vision tasks such as object detection and segmentation.

ResNet-18 is a residual network, which means that it has identity connections between some of its layers. These identity connections help to prevent the network from becoming too deep and overfitting the training data. ResNet-18 has around 11 million trainable parameters, and it is pre-trained on the ImageNet dataset.

ResNet-18 is a powerful CNN architecture that can be used for a variety of image classification tasks. It is a good choice for beginners who want to learn about CNNs, and it is also a good choice for experienced practitioners who need a reliable and efficient CNN model.

Some of the applications of ResNet-18:

• Image classification

• Object detection

• Segmentation

• Natural language processing

• Medical imaging

• Robotics

What about PyTorch, and how it can work with ResNet models?

PyTorch is an open-source machine learning framework based on the Torch library. It is used for applications such as computer vision, natural language processing, and robotics. PyTorch is a popular choice for deep learning research and development because it is flexible and efficient.

PyTorch and ResNet-18 can be used together to build a powerful image classification model. Here are the steps involved:

1. Import the necessary libraries.

2. Load the ResNet-18 model from the torchvision library.

3. Load the pre-trained weights for the ResNet-18 model.

4. Prepare the input data for the ResNet-18 model.

5. Forward pass the input data through the ResNet-18 model.

6. Get the predictions from the ResNet-18 model.

7. Evaluate the performance of the ResNet-18 model.

That's it. This is also my final project at Cognitive Class at IBM. I would like to thank you Joseph Santarcangelo and Alex Aklson, who are the authors of the program and have a supportive guide to AI practitioners.