BiT-50: Big Transfer Model
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | PyTorch |
| Paper | View Paper |
| Downloads | 5,821 |
What is bit-50?
BiT-50 is an advanced implementation of the Big Transfer (BiT) architecture, developed by Google for general visual representation learning. It represents a significant advancement in transfer learning for computer vision tasks, built upon a modified ResNetv2 architecture. The model demonstrates exceptional performance across various data regimes, from scenarios with just one example per class to those with millions of examples.
Implementation Details
The model utilizes a scaled-up pre-training approach with a carefully selected component combination. It's implemented in PyTorch and operates on the ImageNet-1K dataset, achieving an impressive 87.5% top-1 accuracy. The architecture employs transfer learning techniques that significantly improve sample efficiency and simplify hyperparameter tuning.
- Built on modified ResNetv2 architecture
- Pre-trained on large supervised datasets
- Optimized for transfer learning tasks
- Supports various input scales and data regimes
Core Capabilities
- Achieves 87.5% top-1 accuracy on ILSVRC-2012
- 99.4% accuracy on CIFAR-10
- 76.3% on Visual Task Adaptation Benchmark
- Excellent performance with limited data (76.8% on ILSVRC-2012 with just 10 examples per class)
- Robust image classification across diverse scenarios
Frequently Asked Questions
Q: What makes this model unique?
BiT-50 stands out for its exceptional transfer learning capabilities and remarkable performance across different data regimes. It's particularly notable for achieving high accuracy even with very few training examples, making it ideal for real-world applications where labeled data might be scarce.
Q: What are the recommended use cases?
The model is primarily designed for image classification tasks and transfer learning applications. It's particularly effective for scenarios requiring robust visual representation learning, from small-scale tasks with limited data to large-scale deployments requiring high accuracy.
