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pipeline_tag: image-classification
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# VisionLLaMA-Base-MAE
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With the Masked Autoencoders' paradigm, VisionLLaMA-Base-MAE model is trained on ImageNet-1k without labels. It manifests substantial improvements over classification tasks (SFT, linear probing) on ImageNet-1K and the segmentation task on ADE20K.
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| VisionLLaMA-Base-MAE (ep1600) |84.3 | 71.7| 50.2 |
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- mIoU
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pipeline_tag: image-classification
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---
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# VisionLLaMA-Base-MAE
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With the Masked Autoencoders' paradigm, VisionLLaMA-Base-MAE model is trained on ImageNet-1k without labels. It manifests substantial improvements over classification tasks (SFT, linear probing) on ImageNet-1K and the segmentation task on ADE20K.
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| VisionLLaMA-Base-MAE (ep1600) |84.3 | 71.7| 50.2 |
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# How to Use
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Please refer the [Github](https://github.com/Meituan-AutoML/VisionLLaMA) page for usage.
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# Citation
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```
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@article{chu2024visionllama,
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title={VisionLLaMA: A Unified LLaMA Interface for Vision Tasks},
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author={Chu, Xiangxiang and Su, Jianlin and Zhang, Bo and Shen, Chunhua},
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journal={arXiv preprint arXiv:2403.00522},
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year={2024}
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}
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```
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