BEiT Base Patch16-224
| Property | Value |
|---|---|
| Parameter Count | 87M |
| License | Apache 2.0 |
| Paper | View Paper |
| Author | Microsoft |
What is beit-base-patch16-224?
BEiT (BERT Pre-Training of Image Transformers) is a Vision Transformer model that implements a BERT-like self-supervised pre-training approach for image processing. This base model variant processes images at 224x224 resolution using 16x16 pixel patches. It was pre-trained on ImageNet-21k (14M images) and fine-tuned on ImageNet-1k (1M images).
Implementation Details
The model uses a transformer encoder architecture with several key innovations:
- Processes images as 16x16 pixel patches with linear embeddings
- Employs relative position embeddings instead of absolute positions
- Uses mean-pooling of final hidden states for classification
- Performs self-supervised pre-training using masked patch prediction
Core Capabilities
- High-quality image classification across 1,000 ImageNet classes
- Feature extraction for downstream vision tasks
- Efficient processing of 224x224 resolution images
- Robust performance due to large-scale pre-training
Frequently Asked Questions
Q: What makes this model unique?
BEiT uniquely adapts BERT's masked prediction approach to vision tasks, using visual tokens from DALL-E's VQ-VAE encoder as prediction targets. This creates a more sophisticated pre-training objective compared to traditional vision models.
Q: What are the recommended use cases?
The model is best suited for image classification tasks, feature extraction, and transfer learning applications. It's particularly effective when working with standard resolution images (224x224) and when pre-trained visual representations are needed.
