twins_pcpvt_base.in1k
| Property | Value |
|---|---|
| Parameter Count | 43.8M |
| License | Apache 2.0 |
| Paper | Twins: Revisiting the Design of Spatial Attention in Vision Transformers |
| Image Size | 224 x 224 |
| GMACs | 6.7 |
What is twins_pcpvt_base.in1k?
twins_pcpvt_base.in1k is an advanced vision transformer model that reimagines spatial attention mechanisms in computer vision. Developed by researchers at Meituan AutoML, this model represents a significant evolution in the vision transformer architecture, specifically designed for efficient image classification and feature extraction.
Implementation Details
The model implements a sophisticated architecture with 43.8M parameters and operates on 224x224 pixel images. It features a unique spatial attention mechanism that balances computational efficiency with performance, requiring 6.7 GMACs for inference. The model has been pre-trained on the ImageNet-1k dataset, making it particularly effective for general image classification tasks.
- Optimized spatial attention mechanism for improved efficiency
- Supports both classification and feature extraction workflows
- Implements PyTorch framework with Safetensors support
- Pre-trained on ImageNet-1k with verified performance
Core Capabilities
- High-accuracy image classification
- Feature backbone extraction for downstream tasks
- Efficient processing of 224x224 images
- Flexible integration with both classification and embedding generation
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its revised spatial attention mechanism, which improves upon traditional vision transformer architectures while maintaining efficiency. With 43.8M parameters, it strikes a balance between model complexity and performance.
Q: What are the recommended use cases?
The model excels in image classification tasks and can be used as a feature extractor for various computer vision applications. It's particularly well-suited for applications requiring robust image understanding with moderate computational resources.
