google-research/big_vision

Features

• Distributed Training Sharding - Distributes model parameters and optimizer states across multi-host TPU and GPU clusters to enable training of massive architectures.
• Large Scale Training - Trains massive vision transformer models across distributed TPU and GPU hardware using sharded parameters.
• Dataset Integration - Integrates large-scale image and text datasets into the model training pipeline.
• Dataset Preparation Utilities - Includes scripts for downloading and formatting large-scale external image datasets for model consumption.
• Decoder Architectures - Utilizes a decoder-only transformer architecture for autoregressive multimodal sequence generation.
• Transformer Embedding Extraction - Generates high-dimensional embeddings from images for use in multimodal language models or classification tasks.
• Feature Fusion Architectures - Implements architectural patterns for merging visual features and textual tokens into a shared sequence.
• Text-to-Image Generators - Produces high-fidelity images from text prompts using a normalizing flow model as a decoder.
• Multi-Node Training Scaling - Distributes training across single or multi-host setups using GPUs and TPUs.
• Image Data Preprocessing - Defines preprocessing sequences including decoding, cropping, and resizing for image datasets.
• Image Generation - Produces high-fidelity images from text prompts or class labels using normalizing flows and variational autoencoders.
• Conditional Image Generation - Generates synthetic images guided by class labels using latent sequences from a VAE.
• Contrastive Pre-training - Trains and evaluates models that learn shared representations between images and text through contrastive learning.
• Pre-trained Model Checkpoints - Loads pre-trained weights for various architectures and scales to serve as backbones for experiments.
• Data Engineering Pipelines - Implements scalable data loading and engineering pipelines for processing massive datasets.
• TPU Training Accelerators - Coordinates large-scale training workloads across clusters of tensor processing units.
• Vision Model Training - Executes machine learning experiments using configurable architectures and training schedules on distributed hardware.
• Vision-Language Pretraining - Develops contrastive and generative models that map images and text into shared latent spaces during pretraining.
• Model Training Toolkits - Provides a comprehensive framework for sharding parameters and managing pipelines to train massive neural networks.
• Vector-Quantized VAEs - Employs vector-quantized variational autoencoders to represent images as discrete codewords or latent vectors.
• Multimodal Models - Develops architectures that process and generate both text and images using shared embeddings.
• Normalizing Flow Encoders - Uses normalizing flow encoders to transform image data into high-fidelity latent representations.
• Multilingual Image-Text Alignment - Maps images and text into a shared space using captioning-based pretraining and self-supervised losses.
• Vision Transformers - Implements scaling and deployment of vision transformer architectures across distributed GPU and TPU clusters.
• Encoder-Decoder Architectures - Builds large-scale vision architectures using encoder-decoder blocks and multi-head attention for image patches.
• Multimodal Large Language Models - Serves as a research framework for training large-scale multimodal models that process images and text.
• Multimodal Pretraining - Trains multimodal models across stages to increase resolution and sequence length.
• Training Data Pipelines - Provides scalable pipelines for loading and preprocessing images and text into model-ready formats for training.
• Vision Dataset Loading - Integrates standardized image and question-answering datasets for training and evaluating large-scale vision models.
• Depth Estimation - Predicts pixel-dense depth information from images by processing real-valued latent representations.
• Panoptic Segmentation - Identifies and segments all image instances by combining a VAE and a transformer.
• Scalable Generative AI Model Training - Trains large-scale decoder-only transformers to generate and understand both text and images by maximizing data likelihood.
• Sharpness-Aware Minimization - Calculates gradients using sharpness-aware minimization to improve model generalization.
• Variable Resolution Handling - Processes images with varying resolutions and aspect ratios to maintain visual fidelity.
• Multimodal Perception Models - Performs complex visual perception tasks by integrating normalizing flow encoders within transformer architectures.
• Computer Vision - Enables solving visual perception tasks such as panoptic segmentation and depth estimation using pretrained vision backbones.
• Vision Model Fine-Tuning - Adapts large pretrained vision and language models to specific downstream datasets through transfer learning and distillation.
• Model Distillation Tools - Transfers knowledge from large teacher models to smaller student models to improve efficiency.
• Multi-Task Vision Training - Trains unified vision models for segmentation, colorization, and depth prediction using a guiding code approach.
• Vision-Language Fine-Tunings - Transfers a pretrained base model to tasks like captioning or detection through targeted training.
• Multilingual Inference - Enables zero-shot predictions and interactive queries across various languages and visual tasks.
• Pre-trained Model Transfer - Adapts a pre-trained vision model to a new dataset by resetting the output head.
• Real-Valued Generative Models - Produces continuous real-valued entries using multivariate Gaussian mixture models instead of discrete tokens.
• Sharpness-Aware Minimization - Implements sharpness-aware minimization to improve the generalization of large-scale models.
• Dynamic Image Patching - Dynamically adjusts vision model patch sizes to maintain weight integrity during model reuse.
• Dynamic Embedding Resizing - Adjusts image patch dimensionality dynamically to maintain weight compatibility across different resolutions.
• Zero-Shot Classification Models - Categorizes images into classes without specific label training by computing embeddings from pretrained models.
• Vision Model Fine-Tuning - Adapts pre-trained vision models to new datasets through a dedicated transfer script and configuration system.

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