| Property | Value |
|---|---|
| Parameter Count | 15.45M |
| Top-1 Accuracy | 82.93% |
| Image Size | 256x256 |
| License | Apache 2.0 |
| Paper | MaxViT: Multi-Axis Vision Transformer |
What is maxvit_nano_rw_256.sw_in1k?
maxvit_nano_rw_256.sw_in1k is a lightweight variant of the MaxViT architecture, specifically optimized for 256x256 resolution images. It implements a hybrid approach combining convolutional neural networks and transformer architectures, achieving an impressive balance between model size (15.45M parameters) and performance (82.93% top-1 accuracy on ImageNet-1k).
Implementation Details
The model utilizes a multi-axis attention mechanism that combines both local and global feature processing. It's built on the MaxViT architecture which incorporates:
- MBConv (depthwise-separable) convolution blocks
- Dual self-attention mechanisms with window and grid partitioning
- Optimized for PyTorch with RW (Ross Wightman) specific configurations
- 4.46 GMACs computational complexity
- 30.28M activations
Core Capabilities
- Image Classification on ImageNet-1k dataset
- Feature extraction with multiple resolution outputs
- Efficient processing with 1,218.17 samples/sec throughput
- Balanced performance for edge deployment scenarios
Frequently Asked Questions
Q: What makes this model unique?
This model represents an optimal trade-off between model size and performance, specifically designed for scenarios requiring efficient inference on 256x256 images. Its unique multi-axis attention mechanism allows it to capture both local and global features effectively while maintaining a relatively small parameter count.
Q: What are the recommended use cases?
The model is well-suited for: 1) Resource-constrained environments requiring decent classification performance, 2) Real-time image classification tasks, 3) Feature extraction for downstream computer vision tasks, and 4) Scenarios where 256x256 resolution is sufficient for the application needs.