Patch
A patch is a localized segment or subregion of data treated as a unit for representation or processing.
Build a Text-to-Image Generator (from Scratch)
An image patch is a spatially localized portion of an image represented separately in a grid or latent representation.
- How it is used
- Turns images into token sequences for vision-transformer classification, image captioning and transformer-based text-to-image generation.
- How deep it goes
- Works through grid sizing, tensor shapes, PyTorch patch extraction and embedding, then distinguishes pixel-derived tokens from quantized latent patches.
- Examples
- Uses 32 × 32 CIFAR-10 images yielding 64 patches and 128 × 128 captioning images yielding 256 patches.
- What you can do after
- Build it from scratch as a PyTorch patch-embedding pipeline for a vision transformer and extract patch sequences for an image encoder.
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Deep Learning with R, Third Edition
An image patch is a localized rectangular portion of an image processed as an individual visual input unit.
- How it is used
- Uses localized image regions as reusable inputs for convolution, visual sequence processing, and multimodal Transformer representations.
- How deep it goes
- Defines patch size, channel depth, movement across feature maps, flattening into vectors, and encoding as soft tokens.
- Examples
- Uses 3 × 3 convolution patches, 5 × 5 feature-map placement, Vision Transformer sequences, and 256 image patches.
- What you can do after
- Apply patches to understand local convolutional processing and image-to-sequence representations in visual models.
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Deep Learning with Python, Third Edition
A local subregion of an image that is extracted and treated as one sequence element in ViT processing.
- How it is used
- Represents local image content for convolutional processing or sequence-based processing in Vision Transformers and multimodal models.
- How deep it goes
- Defines patches as extracted local regions and flattened sequence elements, but defers detailed Transformer mechanics and patch-generation code.
- Examples
- Uses convolution windows, flattened ViT image sequences, and Gemma’s sequence of 256 image patches.
- What you can do after
- Evaluate/understand how image regions become convolution inputs, ViT sequence elements, or multimodal soft tokens.
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Time Series Forecasting Using Foundation Models
A patch is a segment of consecutive time-series observations grouped as one model input unit.
- How it is used
- Uses grouped observations to preserve local information and reduce transformer input tokens in forecasting architectures.
- How deep it goes
- Explains grouping, tokenization, embedding, token reduction, and frequency-dependent lengths, without presenting implementation code.
- Examples
- Demonstrates length-two patches, frequency-specific patch lengths, and text prototypes describing local trends.
- What you can do after
- Apply patching before embedding to reduce token counts and retain local time-series information.
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How Large Language Models Work
A localized rectangular portion of an image converted into a numerical representation for processing by a model.
- How it is used
- Represents images as sequences of vectors that transformer-based models can process.
- How deep it goes
- Explains extraction, RGB vectorization, preprocessing, and reconstruction, but omits detailed neural-network mathematics.
- Examples
- Uses 16 × 16 RGB tiles, each converted into a 768-value vector.
- What you can do after
- Evaluate how an image becomes transformer input and how model outputs can return to patch-shaped image data.
Related concepts
- Vision Transformerused by · A Vision Transformer is a Transformer-based neural-network architecture that processes images as sequences of tokenized regions using attention.
- PyTorch Unfoldused by · PyTorch Unfold is a tensor operation that extracts sliding local blocks from an input tensor.
- embeddingproduces · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
- encoderproduced by · An encoder is a component that transforms input data into an encoded representation for subsequent processing, reconstruction, comparison, or transmission.
- VQGAN decoderproduced by · A VQGAN decoder is the image-reconstruction component that maps discrete image tokens to an output image.
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