# google-research/vision_transformer **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/google-research-vision-transformer).** 12,309 stars · 1,438 forks · Jupyter Notebook · apache-2.0 ## Links - GitHub: https://github.com/google-research/vision_transformer - awesome-repositories: https://awesome-repositories.com/repository/google-research-vision-transformer.md ## Description This project is a research-focused toolkit for deep learning image classification and multimodal analysis. It provides a library of transformer-based architectures and multi-layer perceptron models designed to process visual data by treating images as sequences of patches rather than relying on traditional convolutional operations. The framework distinguishes itself by enabling cross-modal analysis through a shared latent vector space, which allows for image-text retrieval and zero-shot classification. By mapping visual and textual inputs into a unified numerical representation, the library facilitates direct similarity comparisons between different media types without requiring task-specific training data. The toolkit supports a range of research workflows, including the fine-tuning of pre-trained model checkpoints on custom datasets and the evaluation of model performance. Users can access a collection of serialized model weights to initialize projects, while command-line utilities provide automation for adapting these models to specific visual recognition tasks. ## Tags ### Artificial Intelligence & ML - [MLP-Mixer Processors](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/image-processing/mlp-mixer-processors.md) — Extracts and classifies visual data using multi-layer perceptron architectures applied to image patches. ([source](https://google-research.github.io/vision/_transformer/lit/](https://google-research.github.io/vision_transformer/lit/)) - [Computer Vision Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-toolkits.md) — Provides a research-oriented toolkit for fine-tuning models and evaluating zero-shot classification performance. - [Visual-Textual Alignments](https://awesome-repositories.com/f/artificial-intelligence-ml/cross-modal-representations/visual-textual-alignments.md) — Maps visual and textual inputs into a shared latent vector space for cross-modal similarity comparisons. - [Transformer-Based Image Classifiers](https://awesome-repositories.com/f/artificial-intelligence-ml/image-classification/transformer-based-image-classifiers.md) — Categorizes visual content using transformer-based architectures that identify complex patterns within image pixels. ([source](https://google-research.github.io/vision/_transformer/lit/](https://google-research.github.io/vision_transformer/lit/)) - [MLP-Mixer Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/multilayer-perceptrons/mlp-mixer-architectures.md) — Implements MLP-Mixer architectures to extract visual features by applying fully connected layers across spatial dimensions. - [Multimodal Embedding Models](https://awesome-repositories.com/f/artificial-intelligence-ml/multimodal-embedding-models.md) — Generates shared vector representations of images and text to enable cross-modal retrieval and similarity analysis. - [Vision Transformers](https://awesome-repositories.com/f/artificial-intelligence-ml/vision-transformers.md) — Builds high-performance image classification pipelines using transformer-based architectures and attention mechanisms. - [Zero-Shot Classification Models](https://awesome-repositories.com/f/artificial-intelligence-ml/zero-shot-classification-models.md) — Predicts image categories by calculating similarity between visual embeddings and text labels without task-specific training. ([source](https://github.com/google-research/vision_transformer/blob/main/model_cards/lit.md)) - [Zero-Shot Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/zero-shot-inference.md) — Enables zero-shot image classification and retrieval by calculating similarity between visual and textual embeddings without requiring task-specific training. ([source](https://github.com/google-research/vision_transformer#readme)) - [Image Patch Embedders](https://awesome-repositories.com/f/artificial-intelligence-ml/image-convolution-operations/image-patch-embedders.md) — Divides input images into sequences of flattened patches to enable transformer-based visual processing. - [Image Retrieval Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/image-retrieval-systems.md) — Identifies relevant images for text queries by calculating similarity between their vector representations. ([source](https://github.com/google-research/vision_transformer/blob/main/model_cards/lit.md)) - [Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning.md) — Provides command-line utilities for adapting pre-trained model checkpoints to custom visual datasets. ([source](https://github.com/google-research/vision_transformer#readme)) - [Transformer Encoders](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-encoders.md) — Processes visual token sequences through stacked attention and feed-forward layers for high-level feature representation. - [Attention Mechanisms](https://awesome-repositories.com/f/artificial-intelligence-ml/attention-mechanisms.md) — Implements self-attention mechanisms to compute global dependencies between image patches across the visual field. - [Pre-trained Model Checkpoints](https://awesome-repositories.com/f/artificial-intelligence-ml/large-scale-model-training/vision-transformer-pre-training/pre-trained-model-checkpoints.md) — Provides access to a library of pre-trained model weights to jumpstart visual recognition tasks without training from scratch. ([source](https://github.com/google-research/vision_transformer#readme)) - [Model Checkpoints](https://awesome-repositories.com/f/artificial-intelligence-ml/model-checkpoints.md) — Provides utilities for loading and initializing neural networks from serialized pre-trained model weights. ### Repository Format - [Awesome List](https://awesome-repositories.com/f/repository-format/awesome-list.md) — A community-curated directory that catalogs and links out to other open-source projects, rather than a standalone tool you run yourself. ### Education & Learning Resources - [MLP-Mixer Research](https://awesome-repositories.com/f/education-learning-resources/educational-resources/reference-and-media/books-docs-reference/programming-research-papers/software-architecture-research/mlp-mixer-research.md) — Supports research workflows focused on feature extraction and classification using MLP-Mixer architectures.