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Model Card for InternVL

This repository contains the PyTorch version of the InternVL model weights.

What is InternVL?

[Paper] [GitHub] [Chat Demo]

InternVL scales up the ViT to 6B parameters and aligns it with LLM.

It is the largest open-source vision/vision-language foundation model (14B) to date, achieving 32 state-of-the-art performances on a wide range of tasks such as visual perception, cross-modal retrieval, multimodal dialogue, etc.

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Pretrained Weights

model name type download size
InternViT-6B-224px pytorch 🤗 HF link 12 GB
InternVL-C-13B-224px pytorch 🤗 HF link 25.4 GB

Linear-Probe Image Classification (ImageNet Series)

model name IN-1K IN-ReaL IN-V2 IN-A IN-R IN-Sketch download
InternViT-6B-224px 88.2 90.4 79.9 77.5 89.8 69.1 ckpt | log

Semantic Segmentation (ADE20K)

type backbone head mIoU config download
few-shot (1/16) InternViT-6B Linear 46.5 config ckpt | log
few-shot (1/8) InternViT-6B Linear 50.0 config ckpt | log
few-shot (1/4) InternViT-6B Linear 53.3 config ckpt | log
few-shot (1/2) InternViT-6B Linear 55.8 config ckpt | log
few-shot (1/1) InternViT-6B Linear 57.2 config ckpt | log
linear probing InternViT-6B (frozen) Linear 47.2 config ckpt | log
head tuning InternViT-6B (frozen) UperNet 54.9 config ckpt | log
full tuning InternViT-6B UperNet 58.9 config ckpt | log

License

This project is released under the MIT license. Parts of this project contain code and models from other sources, which are subject to their respective licenses.

Citation

If you find this project useful in your research, please consider cite:

@article{chen2023internvl,
  title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks},
  author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
  journal={arXiv preprint arXiv:2312.14238},
  year={2023}
}

Acknowledgement

InternVL is built with reference to the code of the following projects: OpenAI CLIP, Open CLIP, CLIP Benchmark, EVA, InternImage, ViT-Adapter, MMSegmentation, Transformers, DINOv2, BLIP-2, Qwen-VL, and LLaVA-1.5. Thanks for their awesome work!

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