Internvit 300M 448px

🚀 InternViT-300M-448px

InternViT-300M-448px主要聚焦于提升视觉基础模型的效率。该模型通过从强大的视觉基础模型 InternViT-6B-448px-V1-5 中进行知识蒸馏而开发。它继承了前者强大的鲁棒性、OCR能力和高分辨率处理能力。

[📂 GitHub] [📜 InternVL 1.0] [📜 InternVL 1.5] [📜 Mini-InternVL] [📜 InternVL 2.5]

[🆕 Blog] [🗨️ Chat Demo] [🤗 HF Demo] [🚀 快速开始] [📖 文档]

🚀 快速开始

⚠️ 重要提示

经测试，InternViT V2.5系列更适合用于构建多模态大语言模型（MLLMs），而非传统的计算机视觉任务。

import torch
from PIL import Image
from transformers import AutoModel, CLIPImageProcessor

model = AutoModel.from_pretrained(
    'OpenGVLab/InternViT-300M-448px',
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True).cuda().eval()

image = Image.open('./examples/image1.jpg').convert('RGB')

image_processor = CLIPImageProcessor.from_pretrained('OpenGVLab/InternViT-300M-448px')

pixel_values = image_processor(images=image, return_tensors='pt').pixel_values
pixel_values = pixel_values.to(torch.bfloat16).cuda()

outputs = model(pixel_values)

✨ 主要特性

本次更新主要致力于提升视觉基础模型的效率。通过从强大的视觉基础模型 InternViT-6B-448px-V1-5 中进行知识蒸馏，开发出了InternViT-300M-448px。和其前身一样，InternViT-300M-448px具有448×448的动态输入分辨率，基本图块大小为448×448。训练时允许1到12个图块，测试时扩展到1到40个图块。此外，它继承了InternViT-6B-448px-V1-5强大的鲁棒性、OCR能力和高分辨率处理能力。

📚 详细文档

模型详情

📄 许可证

本项目采用MIT许可证发布。

🔖 引用

如果您在研究中发现本项目很有用，请考虑引用以下文献：

@article{chen2024expanding,
  title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling},
  author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others},
  journal={arXiv preprint arXiv:2412.05271},
  year={2024}
}
@article{gao2024mini,
  title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance},
  author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others},
  journal={arXiv preprint arXiv:2410.16261},
  year={2024}
}
@article{chen2024far,
  title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
  author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
  journal={arXiv preprint arXiv:2404.16821},
  year={2024}
}
@inproceedings{chen2024internvl,
  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 others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={24185--24198},
  year={2024}
}