MobileViT-Small
| Property | Value |
|---|---|
| Parameters | 5.6M |
| Top-1 Accuracy | 78.4% |
| License | Apple Sample Code License |
| Paper | View Paper |
What is mobilevit-small?
MobileViT-small is a lightweight vision transformer model developed by Apple that combines the efficiency of MobileNetV2-style layers with transformer-based global processing. This innovative architecture achieves an impressive balance between performance and computational efficiency, making it particularly suitable for mobile applications.
Implementation Details
The model processes images through a hybrid architecture that combines convolutional neural networks with transformer blocks. Unlike traditional ViT models, MobileViT doesn't require positional embeddings and can process images at various resolutions (160x160 to 320x320).
- Trained on ImageNet-1k dataset for 300 epochs
- Uses multi-scale sampling during training
- Implements cosine annealing learning rate schedule
- Processes images in BGR format with normalization to [0,1]
Core Capabilities
- Image classification across 1000 ImageNet classes
- Efficient inference on mobile devices
- Multi-scale feature processing
- Flexible input resolution handling
Frequently Asked Questions
Q: What makes this model unique?
MobileViT-small uniquely combines CNN efficiency with transformer capabilities, achieving 78.4% top-1 accuracy on ImageNet while maintaining a small parameter count of 5.6M, making it highly efficient for mobile deployments.
Q: What are the recommended use cases?
The model is ideal for mobile image classification tasks, embedded systems, and applications requiring efficient inference with reasonable accuracy. It's particularly suitable for real-world applications where computational resources are limited.
