MobileCLIP
| Property | Value |
|---|---|
| License | Apple ASCL |
| Paper | CVPR 2024 |
| Framework | Core ML |
| Dataset | DataCompDR-1B |
What is coreml-mobileclip?
MobileCLIP is a groundbreaking image-text model developed by Apple that achieves state-of-the-art performance while maintaining exceptional efficiency. The model comes in multiple variants, with the smallest version (MobileCLIP-S0) matching OpenAI's ViT-B/16 performance while being 4.8x faster and 2.8x smaller.
Implementation Details
The model is implemented in Core ML, Apple's machine learning framework, making it optimized for Apple devices. It features both text and image encoders, with different variants offering various performance-speed tradeoffs. The largest variant, MobileCLIP-B(LT), achieves an impressive 77.2% zero-shot ImageNet performance.
- Multiple model variants from S0 to B(LT)
- Combined image and text encoding capabilities
- Optimized latency-performance trade-off
- Core ML compatibility for Apple ecosystem
Core Capabilities
- Fast image-text processing with low latency (as low as 1.5ms + 1.6ms for S0)
- Zero-shot image classification
- Efficient parameter usage (11.4M + 42.4M for S0 to 86.3M + 63.4M for B variant)
- Multi-modal learning capabilities
Frequently Asked Questions
Q: What makes this model unique?
MobileCLIP stands out for its exceptional efficiency-to-performance ratio, achieving similar or better results than larger models while requiring significantly less computational resources and maintaining lower latency.
Q: What are the recommended use cases?
The model is ideal for image-text matching tasks, zero-shot image classification, and multi-modal applications on Apple devices where efficiency and performance are crucial.
