MobileCLIP-S2-OpenCLIP
| Property | Value |
|---|---|
| Parameters | 99.1M (35.7M image + 63.4M text) |
| License | Apple ASCL |
| Paper | MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training |
| Training Samples | 13B |
What is MobileCLIP-S2-OpenCLIP?
MobileCLIP-S2-OpenCLIP is a state-of-the-art vision-language model developed by Apple that achieves remarkable efficiency in zero-shot image classification tasks. Part of the MobileCLIP family, the S2 variant represents an optimal balance between performance and computational efficiency, achieving 74.4% accuracy on ImageNet while being significantly faster and smaller than comparable models.
Implementation Details
The model implements a novel architecture that combines efficient image processing with powerful text understanding capabilities. It utilizes 35.7M parameters for image processing and 63.4M parameters for text processing, with a combined latency of just 6.9ms (3.6ms for image + 3.3ms for text processing).
- Optimized architecture for mobile and efficient deployment
- Multi-modal reinforced training approach
- 13B training samples for robust performance
- Zero-shot classification capabilities
Core Capabilities
- 74.4% top-1 accuracy on ImageNet-1K zero-shot classification
- 63.7% average performance across 38 datasets
- 2.3x faster than comparable ViT-B/16 models
- 2.1x smaller model size compared to similar performers
Frequently Asked Questions
Q: What makes this model unique?
MobileCLIP-S2 stands out for its exceptional efficiency-to-performance ratio, achieving comparable or better results than larger models while requiring significantly less computational resources. It's particularly notable for achieving better average zero-shot performance than SigLIP's ViT-B/16 model despite being more than twice as fast and smaller.
Q: What are the recommended use cases?
The model is ideal for zero-shot image classification tasks, particularly in scenarios where computational efficiency is crucial. It's well-suited for mobile applications, real-time processing, and large-scale deployment where both speed and accuracy are important.
