Big Transfer (BiT)
Official repository for Big Transfer (BiT) models, providing pre-trained visual representations for efficient transfer learning across computer vision tasks.
What is Big Transfer (BiT)?
Big Transfer (BiT) is an open-source project from Google Research that provides pre-trained deep learning models for computer vision tasks. It focuses on transfer learning, allowing developers to fine-tune these models on custom datasets with minimal data and computational resources. The models are trained on large-scale datasets like ImageNet-21k to capture general visual representations that boost performance across various downstream applications.
Machine learning researchers, computer vision engineers, and data scientists who need efficient, high-accuracy models for image classification, few-shot learning, or benchmark tasks like VTAB-1k. It's also suitable for educators and practitioners exploring transfer learning techniques.
BiT offers state-of-the-art pre-trained models with multi-framework support (TensorFlow, PyTorch, Jax), reducing training time and data requirements. Its emphasis on large-scale pre-training and knowledge distillation provides a unique balance of performance and efficiency, backed by rigorous research and extensive benchmarking.
Overview
Official repository for the "Big Transfer (BiT): General Visual Representation Learning" paper.
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