MobileViT XS
| Property | Value |
|---|---|
| Parameter Count | 2.3M |
| Model Type | Vision Transformer |
| License | Other (See ml-cvnets) |
| Paper | MobileViT Paper |
| Dataset | ImageNet-1k |
What is mobilevit_xs.cvnets_in1k?
MobileViT XS is a lightweight, mobile-friendly vision transformer designed for efficient image classification. Developed by Apple, it represents a breakthrough in deploying transformer architectures on resource-constrained devices while maintaining competitive performance.
Implementation Details
The model features a compact architecture with only 2.3M parameters and requires 1.1 GMACs for inference. It processes images at 256x256 resolution and generates 16.3M activations. The architecture combines the efficiency of mobile-first design with the powerful attention mechanisms of vision transformers.
- Optimized for mobile deployment with minimal computational overhead
- Supports feature map extraction with multiple resolution outputs
- Provides image embedding capabilities with 384-dimensional feature vectors
- Implements efficient attention mechanisms for visual processing
Core Capabilities
- Image Classification: Primary task with ImageNet-1k training
- Feature Extraction: Supports multi-scale feature map generation
- Embedding Generation: Can output pure image embeddings
- Mobile Deployment: Optimized for resource-constrained environments
Frequently Asked Questions
Q: What makes this model unique?
MobileViT XS stands out for its extremely efficient architecture that successfully combines mobile-first design principles with transformer-based attention mechanisms, achieving a remarkable balance between model size (2.3M parameters) and performance.
Q: What are the recommended use cases?
The model is ideal for mobile and edge device deployment where resources are limited. It's particularly suitable for image classification tasks, feature extraction, and as a backbone for downstream computer vision tasks requiring efficient processing.
