Brief Review — OpenCLIP: Reproducible scaling laws for contrastive language-image learning

OpenCLIP, Open-Sourced CLIP With Scaling Law Study

Sik-Ho Tsang
3 min readMar 19, 2024
OpenCLIP / CLIP (Image from CLIP paper)

Reproducible scaling laws for contrastive language-image learning
OpenCLIP, by LAION; UC Berkeley; HuggingFace; University of Washington; Research Center Juelich (FZJ)
2022 arXiv v1, Over 190 Citations (Sik-Ho Tsang @ Medium)

Visual/Vision/Video Language Model (VLM)
20172022 [FILIP] [Wukong] [LiT] [Flamingo] [FLAVA] [SimVLM] [VLMo] [BEiT-3] [GLIP] 2023 [GPT-4] [GPT-4V(ision)] [MultiModal-CoT] [CoCa]
==== My Other Paper Readings Are Also Over Here ====


  1. LAION-400M/2B Dataset & OpenCLIP
  2. Results

1. LAION-400M/2B Dataset & OpenCLIP

Effect of Data & Model Scaling

With both data and model scaling, OpenCLIP outperforms CLIP.

1.1. LAION Dataset

  • LAION-400M [66] and LAION-5B [65] datasets are used, which are open, public image-text datasets.
  • LAION-5B contains an English image-text subset of 2.32 billion samples, which is referred in this paper as LAION-2B in this work.
  • LAION-80M, which is the subset of LAION-400M, is also used.

1.2. OpenCLIP Training

  • CLIP is trained with different sizes of Transformer & ViT.
  • Global batch sizes are in the range of 86–88K samples.
  • InfoNCE loss is used.
  • Same as OpenAI CLIP, given a dataset {(xi, yi)}ni =1, each image is classified as the class that has the largest cosine similarity with the (L2-normalized) image embedding.

2. OpenCLIP

2.1. Zero-Shot Transfer and Robustness

Figure 1a: Accuracy follows power laws, such that larger models benefit from larger data and samples seen scale.

  • The strongest ImageNet accuracy (78%) is obtained with the largest total pre-training compute, using ViT-H/14 pre-trained on LAION-2B data scale and 34B samples seen.
  • Fitting power-law (E=βC^α) on the Pareto frontier of the available models, we measure scaling coefficients αopenCLIP = -0.11 and αCLIP = -0.16 for zero-shot top-1 ImageNet and αopenCLIP = -0.13 and αCLIP = -0.24 for ImageNet robustness datasets performance.

2.2. Retrieval

(Figure 1b): Again it is observed that performance consistently improves when increasing scale following power law trends.

2.3. Others

ImageNet and CIFAR-100
  • Similar observations for other tasks/datasets.



Sik-Ho Tsang

PhD, Researcher. I share what I learn. :) Linktree: for Twitter, LinkedIn, etc.