Understanding Image Feature Extraction with ResNet152 and VGG19 in PyTorch скачать в хорошем качестве

Understanding Image Feature Extraction with ResNet152 and VGG19 in PyTorch

Discover how to extract features from images using `ResNet152` and `VGG19` models in PyTorch, and understand which layers provide the relevant features for image classification tasks. --- This video is based on the question https://stackoverflow.com/q/70859276/ asked by the user 'Rafi' ( https://stackoverflow.com/u/10681724/ ) and on the answer https://stackoverflow.com/a/70859459/ provided by the user 'kHarshit' ( https://stackoverflow.com/u/6210807/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Image Feature Extraction in PyTorch Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Understanding Image Feature Extraction with ResNet152 and VGG19 in PyTorch Image feature extraction is a crucial step in image classification tasks, particularly when using deep learning frameworks like PyTorch. Recently, many individuals, including new learners in the field, have expressed confusion about how to correctly extract features using popular models such as ResNet152 and VGG19. In this guide, we will explore this topic in detail and clarify any uncertainties around the code snippet provided. The Code Snippet Here’s a look at the code that you might be trying to understand: [[See Video to Reveal this Text or Code Snippet]] In this code, the functions ResNet152 and VGG return models that can be used for image classification. But there's a crucial question: from which part of these models are the features being extracted? Understanding the Model Structure To comprehend where the features are extracted from, we can start by examining the structure of the models returned by these functions. When you call: [[See Video to Reveal this Text or Code Snippet]] You would see an output similar to the following (truncated for brevity): [[See Video to Reveal this Text or Code Snippet]] Key Components of ResNet Convolutional Layer (conv1): The first layer where initial image features are extracted. Pooling Layer (avgpool): This layer reduces the dimensionality and generates a compact representation of features. Fully-Connected Layer (fc): The last layer in the network, which outputs the final classification scores. Feature Extraction Layer In this case, the features are extracted from the model's last fully-connected layer (often referred to as fc). When you pass an image through the model, it is the output from this layer that provides the feature representation for classification tasks. The Case for VGG19 The situation with the VGG model is quite similar. When you print out the VGG model's architecture, you will also find a fully-connected layer at the end that represents the feature extraction of the model. Turbulence in Understanding Many users might feel lost when diving into the specifics of feature extraction. A simple way to keep track is to remember a few key points: Features are obtained from the output layer: This is standard across most pre-trained models. Pre-trained vs. Train From Scratch: If working with a pre-trained model, often the last layer can be adjusted based when using it for different classification tasks. Conclusion By examining how both ResNet152 and VGG19 are structured, it's clear that the last fully-connected layer serves as the primary source for extracted features when it comes to image classification tasks. As you dive deeper into PyTorch and image processing, keeping these key points in mind can streamline your understanding, allowing you to utilize these powerful models more effectively. If you have any further questions or need clarification on specific aspects, feel free to reach out!