MobileViT-S
| Property | Value |
|---|---|
| Parameter Count | 5.6M |
| Model Type | Image Classification / Feature Backbone |
| GMACs | 2.0 |
| Image Size | 256 x 256 |
| License | Other (See ml-cvnets) |
| Paper | MobileViT Paper |
What is mobilevit_s.cvnets_in1k?
MobileViT-S is a revolutionary vision transformer model that combines the efficiency of mobile-friendly architectures with the powerful capabilities of vision transformers. Developed by Apple, this model represents a significant advancement in lightweight, general-purpose computer vision models with just 5.6M parameters while maintaining robust performance on ImageNet-1k.
Implementation Details
The model operates on 256x256 images and utilizes a hybrid architecture that combines traditional convolutional neural networks with transformer mechanisms. It achieves impressive efficiency with only 2.0 GMACs and 19.9M activations, making it particularly suitable for mobile and edge devices.
- Efficient parameter utilization with only 5.6M parameters
- Optimized for 256x256 input images
- Feature extraction capabilities with multiple resolution outputs
- Mobile-friendly architecture with low computational requirements
Core Capabilities
- Image classification with high efficiency
- Feature map extraction at multiple scales
- Image embedding generation
- Flexible integration with both classification and feature extraction pipelines
Frequently Asked Questions
Q: What makes this model unique?
MobileViT-S stands out for its innovative combination of mobile-friendly architecture and transformer capabilities, achieving a remarkable balance between model size (5.6M params) and performance. It's specifically designed to be lightweight while maintaining competitive accuracy on ImageNet-1k.
Q: What are the recommended use cases?
This model is ideal for mobile and edge device applications requiring image classification or feature extraction. It's particularly suitable for scenarios where computational resources are limited but high-quality vision capabilities are needed, such as mobile apps, edge devices, and real-time processing systems.
