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Vision Transformers: Theory and Practical Implementation from Scratch
- In this article, we’ll dive into the theory behind vision transformers, understand why they’re becoming increasingly popular, compare them with CNNs, and discuss when to use them.
- Transformers lack the inherent ability to understand the position of image patches. To compensate for this, positional encodings are added to the patch embeddings, enabling the model to learn the relative positions of patches in an image.
- The strength of vision transformers lies in their ability to model global dependencies and context across the entire image, making them especially effective for large-scale datasets.
- Traditional deep learning models for computer vision — like CNNs — excel at extracting spatial features through convolution operations.
- Vision transformers break this paradigm. Instead of convolutions, they rely on a self-attention mechanism that allows the model to look at the entire image globally, learning relationships between patches regardless of their distance.
- CNNs are ideal for tasks requiring local feature extraction and fast training, while ViTs shine when global context and scalability are key.
- In this section, we’ll explore how to build a vision transformer from scratch to gain a deeper understanding of its architecture and how each component works together.
- Now, let’s take a look at how to use pretrained Vision Transformers (ViTs) with the help of the transformers library from Hugging Face.
- The ViTImageProcessor handles image resizing and normalization according to the model’s requirements.
- ViTs are poised to enhance the performance of AI systems in visual data interpretation and beyond.
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