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EVA-02: A Visual Representation for Neon Genesis

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arxiv 2303.11331 v2 pith:MR5VZR5R submitted 2023-03-20 cs.CV cs.CL

Yuxin Fang , Quan Sun , Xinggang Wang , Tiejun Huang , Xinlong Wang , Yue Cao This is my paper
classification cs.CVcs.CL
keywords eva-02parametersopenvisionaccessibleclipdataimagenet-1k
verification ladder T0 review T1 audit T2 compute T3 formal
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We launch EVA-02, a next-generation Transformer-based visual representation pre-trained to reconstruct strong and robust language-aligned vision features via masked image modeling. With an updated plain Transformer architecture as well as extensive pre-training from an open & accessible giant CLIP vision encoder, EVA-02 demonstrates superior performance compared to prior state-of-the-art approaches across various representative vision tasks, while utilizing significantly fewer parameters and compute budgets. Notably, using exclusively publicly accessible training data, EVA-02 with only 304M parameters achieves a phenomenal 90.0 fine-tuning top-1 accuracy on ImageNet-1K val set. Additionally, our EVA-02-CLIP can reach up to 80.4 zero-shot top-1 on ImageNet-1K, outperforming the previous largest & best open-sourced CLIP with only ~1/6 parameters and ~1/6 image-text training data. We offer four EVA-02 variants in various model sizes, ranging from 6M to 304M parameters, all with impressive performance. To facilitate open access and open research, we release the complete suite of EVA-02 to the community at https://github.com/baaivision/EVA/tree/master/EVA-02.

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Cited by 10 Pith papers

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