MobileCLIP-S0
| Property | Value |
|---|---|
| Architecture | MobileCLIP |
| Parameters | 53.8M (11.4M image + 42.4M text) |
| License | Apple ASCL |
| Paper | MobileCLIP Paper (CVPR 2024) |
What is mobileclip_s0_timm?
MobileCLIP-S0 is a lightweight and efficient image-text model designed for fast multimodal processing. It's the smallest variant in the MobileCLIP family, achieving impressive zero-shot performance comparable to OpenAI's ViT-B/16 while being significantly more efficient.
Implementation Details
The model is built with efficiency in mind, featuring a dual-architecture approach with separate image and text encoders. It processes images and text with remarkably low latency (1.5ms for image and 1.6ms for text processing) while maintaining high performance.
- Zero-shot ImageNet-1K accuracy: 67.8%
- Average performance across 38 datasets: 58.1%
- Training samples: 13B
- TIMM-compatible implementation
Core Capabilities
- Fast image-text processing with minimal latency
- Efficient zero-shot image classification
- Compact model size without sacrificing performance
- Multi-modal understanding and alignment
Frequently Asked Questions
Q: What makes this model unique?
MobileCLIP-S0 stands out for its exceptional efficiency-to-performance ratio, being 4.8x faster and 2.8x smaller than ViT-B/16 while maintaining similar zero-shot performance levels.
Q: What are the recommended use cases?
The model is ideal for resource-constrained environments requiring fast image-text processing, such as mobile applications, real-time classification tasks, and efficient zero-shot learning scenarios.
