title: Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and
    Training Strategies
summary: 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.
paper_authors._id: 661c8b3522ca1dfd3fe43d85
paper_authors.name: Zichao Li
paper_authors._id: 661c8b3522ca1dfd3fe43d86
paper_authors.name: Cihang Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T11:22:33.533Z
paper_authors._id: 645eb61da3c5cd8a16efffff
paper_authors.avatarUrl: /avatars/9112bfeed598dfabf9e077e69e09ecc9.svg
paper_authors.fullname: Cihang Xie
paper_authors.type: user
paper_authors.user: cihangxie
paper_authors._id: 661c8b3522ca1dfd3fe43d87
paper_authors.name: Ekin Dogus Cubuk
paper_title: Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and
    Training Strategies
paper_summary: 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.
paper_ai_summary: Research explores the performance of scaled-down CLIP models, examining data quality, model architecture, and training strategies to optimize resource utilization and performance.
paper_ai_keywords: Contrastive Language-Image Pre-training CLIP high-quality training data dataset sizes ViT models CNN-based architecture ViT-based architecture SLIP FLIP CLIP CLIP+Data Augmentation