We launch EVA, a vision-centric foundation model to explore the limits of
visual representation at scale using only publicly accessible data. EVA is a
vanilla ViT pre-trained to reconstruct the masked out image-text aligned vision
features conditioned on visible image patches. Via this pretext task, we can
efficiently scale up EVA to one billion parameters, and sets new records on a
broad range of representative vision downstream tasks, such as image
recognition, video action recognition, object detection, instance segmentation
and semantic segmentation without heavy supervised training. Moreover, we
observe quantitative changes in scaling EVA result in qualitative changes in
transfer learning performance that are not present in other models. For
instance, EVA takes a great leap in the challenging large vocabulary instance
segmentation task: our model achieves almost the same state-of-the-art
performance on LVISv1.0 dataset with over a thousand categories and COCO
dataset with only eighty categories. Beyond a pure vision encoder, EVA can also
serve as a vision-centric, multi-modal pivot to connect images and text. We
find initializing the vision tower of a giant CLIP from EVA can greatly
stabilize the training and outperform the training from scratch counterpart
with much fewer samples and less compute, providing a new direction for scaling
up and accelerating the costly training of multi-modal foundation models. To
facilitate future research, we release all the code and models at
this https URL.

我们在这篇论文中提出了 EVA，这是一种基于视觉的基础模型，能够大规模地探索视觉表征的极限，并且只使用公开可用的数据。通过解决预先设定的任务，我们可以有效地将 EVA 扩展到 10 亿个参数，并在诸如图像识别、视频动作识别、目标检测、实例分割和语义分割等广泛的代表性视觉下游任务上创造新的记录，而无需进行大量的监督式训练。