This paper investigates the performance of the Contrastive Language-Image Pre-training (CLIP) when scaled down to limited computation budgets. We explore CLIP along three dimensions: data, architecture, and training strategies. With regards to data, we demonstrate the significance of high-quality training data and show that a smaller dataset of high-quality data can outperform a larger dataset with lower quality. We also examine how model performance varies with different dataset sizes, suggesting that smaller ViT models are better suited for smaller datasets, while larger models perform better on larger datasets with fixed compute. Additionally, we provide guidance on when to choose a CNN-based architecture or a ViT-based architecture for CLIP training. We compare four CLIP training strategies - SLIP, FLIP, CLIP, and CLIP+Data Augmentation - and show that the choice of training strategy depends on the available compute resource. Our analysis reveals that CLIP+Data Augmentation can achieve comparable performance to CLIP using only half of the training data. This work provides practical insights into how to effectively train and deploy CLIP models, making them more accessible and affordable for practical use in various applications.

研究通过受限的计算预算缩小规模的对比性语言-图像预训练（CLIP）的性能，分析数据、架构和训练策略三个维度，证明高质量训练数据的重要性，指出较小的数据集在一定计算限制下优于较大数据集，并提供选择基于CNN架构或ViT架构进行CLIP训练的指导。同时比较四种CLIP训练策略，并表明在可用计算资源上选择训练策略的不同，最后发现只使用一半的训练数据，CLIP+数据增强可以实现与CLIP相当的性能。本研究为有效训练和部署CLIP模型提供了实用见解，使其在各种应用中更加可行和经济实惠。

缩放 (下降) CLIP: 数据、架构和训练策略的全面分析