  State-of-the-art computer vision systems are trained to predict a fixed set
of predetermined object categories. This restricted form of supervision limits
their generality and usability since additional labeled data is needed to
specify any other visual concept. Learning directly from raw text about images
is a promising alternative which leverages a much broader source of
supervision. We demonstrate that the simple pre-training task of predicting
which caption goes with which image is an efficient and scalable way to learn
SOTA image representations from scratch on a dataset of 400 million (image,
text) pairs collected from the internet. After pre-training, natural language
is used to reference learned visual concepts (or describe new ones) enabling
zero-shot transfer of the model to downstream tasks. We study the performance
of this approach by benchmarking on over 30 different existing computer vision
datasets, spanning tasks such as OCR, action recognition in videos,
geo-localization, and many types of fine-grained object classification. The
model transfers non-trivially to most tasks and is often competitive with a
fully supervised baseline without the need for any dataset specific training.
For instance, we match the accuracy of the original ResNet-50 on ImageNet
zero-shot without needing to use any of the 1.28 million training examples it
was trained on. We release our code and pre-trained model weights at
https://github.com/OpenAI/CLIP.
