mtgv / VisionLLaMA-Large-MAE

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image-classification

Introduction of VisionLLaMA-Large-MAE

Model Details of VisionLLaMA-Large-MAE

VisionLLaMA-Base-MAE

With the Masked Autoencoders' paradigm, VisionLLaMA-Large-MAE model is trained on ImageNet-1K without labels. It retains improvements over classification tasks (SFT, linear probing) on ImageNet-1K.

Model ImageNet Acc (SFT) ImageNet Acc (Linear Probe)
VisionLLaMA-Large-MAE (ep800) 85.5 77.3

How to Use

Please refer the Github page for usage.

Citation

@article{chu2024visionllama,
  title={VisionLLaMA: A Unified LLaMA Interface for Vision Tasks},
  author={Chu, Xiangxiang and Su, Jianlin and Zhang, Bo and Shen, Chunhua},
  journal={arXiv preprint arXiv:2403.00522},
  year={2024}
}

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VisionLLaMA-Large-MAE huggingface.co Url

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mtgv VisionLLaMA-Large-MAE online free

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VisionLLaMA-Large-MAE install

VisionLLaMA-Large-MAE is an open source model from GitHub that offers a free installation service, and any user can find VisionLLaMA-Large-MAE on GitHub to install. At the same time, huggingface.co provides the effect of VisionLLaMA-Large-MAE install, users can directly use VisionLLaMA-Large-MAE installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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