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Bit 50

Developed by google
BiT is a simple method for scaling up pre-training of ResNet-like architectures, bringing significant improvements in transfer learning.
Downloads 9,766
Release Time : 12/6/2022

Model Overview

The BiT model is a visual representation learning model primarily used for image classification tasks. Through large-scale pre-training and simple transfer methods, it performs exceptionally well on various datasets.

Model Features

Large-scale pre-training
Pre-training on large-scale supervised datasets improves sample efficiency and simplifies hyperparameter tuning.
Simple transfer method
Adopts a straightforward approach called Big Transfer (BiT), combining carefully selected components and heuristic transfer strategies.
Broad applicability
Performs well across a wide range of data quantities, from 1 sample per class to 1 million samples per class.

Model Capabilities

Image classification
Visual representation learning
Transfer learning

Use Cases

Computer vision
ImageNet classification
Classify images into 1000 ImageNet categories
Achieves 87.5% top-1 accuracy on ILSVRC-2012
Few-shot learning
Perform image classification with limited data
Achieves 76.8% accuracy on ILSVRC-2012 with just 10 samples per class
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