| Property | Value |
|---|---|
| Parameter Count | 88M parameters |
| Model Type | Vision Transformer |
| Image Size | 224 x 224 |
| Top-1 Accuracy | 84.63% |
| GMACs | 15.5 |
| Paper | DaViT: Dual Attention Vision Transformers |
What is davit_base.msft_in1k?
DaViT Base is a sophisticated vision transformer model that implements a novel dual attention mechanism for image classification tasks. Trained on the ImageNet-1k dataset, it represents a significant advancement in vision transformer architecture, achieving an impressive 84.63% top-1 accuracy while maintaining computational efficiency.
Implementation Details
The model operates on 224x224 pixel images and utilizes a dual attention mechanism that effectively combines spatial and channel attention. With 88M parameters and 15.5 GMACs, it provides a balanced trade-off between computational cost and performance.
- Employs dual attention mechanism for enhanced feature extraction
- Trained on ImageNet-1k dataset
- Supports feature map extraction at multiple scales
- Provides image embedding capabilities
Core Capabilities
- Image classification with 1000 classes
- Feature extraction at various network depths
- Generation of image embeddings
- Flexible interface for both classification and feature extraction
Frequently Asked Questions
Q: What makes this model unique?
DaViT's dual attention mechanism sets it apart from traditional vision transformers, allowing it to capture both spatial and channel relationships more effectively. This results in strong performance while maintaining reasonable computational requirements.
Q: What are the recommended use cases?
The model is particularly well-suited for image classification tasks, feature extraction, and as a backbone for downstream computer vision tasks. It's ideal for applications requiring high accuracy and the ability to capture complex image features.