mtgv / VisionLLaMA-Base-MAE

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image-classification

Introduction of VisionLLaMA-Base-MAE

Model Details of VisionLLaMA-Base-MAE

VisionLLaMA-Base-MAE

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.

Model ImageNet Acc (SFT) ImageNet Acc (Linear Probe) ADE20K Segmentation
VisionLLaMA-Base-MAE (ep800) 84.0 69.7 49.0
VisionLLaMA-Base-MAE (ep1600) 84.3 71.7 50.2

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-Base-MAE huggingface.co is an AI model on huggingface.co that provides VisionLLaMA-Base-MAE's model effect (), which can be used instantly with this mtgv VisionLLaMA-Base-MAE model. huggingface.co supports a free trial of the VisionLLaMA-Base-MAE model, and also provides paid use of the VisionLLaMA-Base-MAE. Support call VisionLLaMA-Base-MAE model through api, including Node.js, Python, http.

VisionLLaMA-Base-MAE huggingface.co Url

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mtgv VisionLLaMA-Base-MAE online free

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VisionLLaMA-Base-MAE install

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

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