Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and
  Training Strategies
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.
661c8b3522ca1dfd3fe43d85
Zichao Li
661c8b3522ca1dfd3fe43d86
Cihang Xie
admin_assigned
2024-04-18T11:22:33.533Z
645eb61da3c5cd8a16efffff
/avatars/9112bfeed598dfabf9e077e69e09ecc9.svg
Cihang Xie
user
cihangxie
661c8b3522ca1dfd3fe43d87
Ekin Dogus Cubuk
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and
  Training Strategies
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.
Research explores the performance of scaled-down CLIP models, examining data quality, model architecture, and training strategies to optimize resource utilization and performance.
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