MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training

TL;DR

MobileCLIP introduces an efficient image-text model optimized for mobile deployment, utilizing multi-modal reinforced training to enhance accuracy and speed while reducing training overhead, achieving state-of-the-art latency-accuracy tradeoffs.

Contribution

The paper presents MobileCLIP, a novel family of efficient models with a new training approach that transfers knowledge from captioning models and CLIP ensembles, improving mobile performance.

Findings

MobileCLIP-S2 is 2.3× faster and more accurate than previous CLIP models.

Achieves +2.9% average performance on 38 benchmarks.

10×-1000× improved learning efficiency over non-reinforced training.

Abstract

Contrastive pretraining of image-text foundation models, such as CLIP, demonstrated excellent zero-shot performance and improved robustness on a wide range of downstream tasks. However, these models utilize large transformer-based encoders with significant memory and latency overhead which pose challenges for deployment on mobile devices. In this work, we introduce MobileCLIP -- a new family of efficient image-text models optimized for runtime performance along with a novel and efficient training approach, namely multi-modal reinforced training. The proposed training approach leverages knowledge transfer from an image captioning model and an ensemble of strong CLIP encoders to improve the accuracy of efficient models. Our approach avoids train-time compute overhead by storing the additional knowledge in a reinforced dataset. MobileCLIP sets a new state-of-the-art latency-accuracy tradeoff for zero-shot classification and retrieval tasks on several datasets. Our MobileCLIP-S2 variant is 2.3$\times$ faster while more accurate compared to previous best CLIP model based on ViT-B/16. We further demonstrate the effectiveness of our multi-modal reinforced training by training a CLIP model based on ViT-B/16 image backbone and achieving +2.9% average performance improvement on 38 evaluation benchmarks compared to the previous best. Moreover, we show that the proposed approach achieves 10$\times$-1000$\times$ improved learning efficiency when compared with non-reinforced CLIP training. Code and models are available at https://github.com/apple/ml-mobileclip .