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OpenCLIP

Welcome to OpenCLIP, an open-source implementation of OpenAI’s CLIP (Contrastive Language-Image Pre-training). This codebase provides production-ready models for zero-shot image classification, image-text retrieval, and transfer learning tasks.
OpenCLIP is actively maintained by researchers at UW, Google, Stanford, Amazon, Columbia, and Berkeley, with continuous contributions from the open-source community.

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What is CLIP?

CLIP learns visual concepts from natural language supervision by training on image-text pairs. This approach enables powerful zero-shot transfer capabilities, allowing models to classify images into categories they’ve never explicitly seen during training. CLIP Architecture

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Key Features

OpenCLIP provides a comprehensive collection of pretrained models trained on various datasets including LAION-400M, LAION-2B, and DataComp-1B. Models range from efficient mobile architectures to large-scale transformers achieving up to 85.4% zero-shot accuracy on ImageNet.
Battle-tested on up to 1024 A100 GPUs with native support for SLURM clusters. Includes optimizations like gradient accumulation, local loss computation, and efficient memory management for large-scale training.
Perform image classification without training examples. Simply describe the classes in natural language and the model can identify them in images.
  • Vision Transformers (ViT-B, ViT-L, ViT-H, ViT-bigG)
  • ConvNet architectures (ConvNext, ResNet)
  • SigLIP models for improved efficiency
  • CoCa models for generative captioning
Clean, well-documented Python API with support for:
  • Loading models from Hugging Face Hub
  • Custom preprocessing pipelines
  • Mixed precision training (FP16, BF16)
  • JIT compilation
  • WebDataset for large-scale datasets

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State-of-the-Art Results

OpenCLIP models achieve competitive or superior performance compared to proprietary alternatives: View the complete model zoo and zero-shot results across 38 datasets in our model documentation.

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Research Foundation

OpenCLIP is backed by rigorous research on reproducible scaling laws for contrastive language-image learning:
Paper: Reproducible Scaling Laws for Contrastive Language-Image Learning
Published at CVPR 2023
The research demonstrates how model performance scales with:
  • Training compute budget
  • Dataset size and quality
  • Model architecture choices
  • Training hyperparameters

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Use Cases

OpenCLIP powers a wide range of applications:
  • Zero-Shot Classification: Classify images without training data
  • Image-Text Retrieval: Search images using natural language queries
  • Transfer Learning: Fine-tune on downstream tasks with robust pretrained features
  • Embedding Generation: Create semantic embeddings for images and text
  • Content Moderation: Filter and classify visual content
  • Multimodal Search: Build search engines that understand both images and text
  • Data Curation: Automatically label and organize image datasets

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Model Availability

All models are available through multiple channels:
  • PyPI package: open_clip_torch
  • Hugging Face Hub: OpenCLIP library tag
  • Direct download from model zoo
Model cards with additional details are available on Hugging Face Hub.

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Community and Support

OpenCLIP is an active open-source project:
Portions of the modeling and tokenizer code are adaptations of OpenAI’s official CLIP repository.

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Next Steps

1

Install OpenCLIP

Get started by installing the package via pip
2

Try the Quickstart

Run your first zero-shot classification example
3

Explore Models

Browse the pretrained model zoo
4

Train Your Own

Learn how to train CLIP on your own data