This paper introducesAim, a collection of vision models pre-trained with an
autoregressive objective.
These models are inspired by their textual counterparts, i.e., Large Language Models (LLMs) , and exhibit similar scaling properties.
Specifically, we highlight two key findings: (1)  the performance of the visual features scale with both the model capacity and the quantity of data,
(2)  the value of the objective function correlates with the performance of the model on downstream tasks.
We illustrate the practical implication of these findings by pre-training a 7 billion parameterAimon 2 billion images that achieves 84.0% on ImageNet-1k with a frozen trunk.
Interestingly, even at this scale, we observe no sign of saturation in performance, suggesting thatAimpotentially represents a new frontier for
training large-scale vision models.
The pre-training ofAimis similar to the pre-training of LLMs, and does not require any image-specific strategy to stabilize the training at scale.