  In this paper, we propose a novel cross-modal distillation method, called
TinyCLIP, for large-scale language-image pre-trained models. The method
introduces two core techniques: affinity mimicking and weight inheritance.
Affinity mimicking explores the interaction between modalities during
distillation, enabling student models to mimic teachers' behavior of learning
cross-modal feature alignment in a visual-linguistic affinity space. Weight
inheritance transmits the pre-trained weights from the teacher models to their
student counterparts to improve distillation efficiency. Moreover, we extend
the method into a multi-stage progressive distillation to mitigate the loss of
informative weights during extreme compression. Comprehensive experiments
demonstrate the efficacy of TinyCLIP, showing that it can reduce the size of
the pre-trained CLIP ViT-B/32 by 50%, while maintaining comparable zero-shot
performance. While aiming for comparable performance, distillation with weight
inheritance can speed up the training by 1.4 - 7.8 $\times$ compared to
training from scratch. Moreover, our TinyCLIP ViT-8M/16, trained on YFCC-15M,
achieves an impressive zero-shot top-1 accuracy of 41.1% on ImageNet,
surpassing the original CLIP ViT-B/16 by 3.5% while utilizing only 8.9%
parameters. Finally, we demonstrate the good transferability of TinyCLIP in
various downstream tasks. Code and models will be open-sourced at
https://aka.ms/tinyclip.
