File size: 8,655 Bytes
b875345 61d9457 b875345 61d9457 bc86907 61d9457 1d37d35 61d9457 1d37d35 b83d090 021fa53 bc86907 61d9457 4e88ad5 61d9457 bc86907 61d9457 bc86907 61d9457 b800530 54b948b bc86907 54b948b bc86907 54b948b bc86907 54b948b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
---
license: mit
metrics:
- accuracy
- mean_iou
---
# Model Card for InternVL
This repository contains the PyTorch version of the InternVL model weights.
## What is InternVL?
\[[Paper](https://arxiv.org/abs/2312.14238)\] \[[GitHub](https://github.com/OpenGVLab/InternVL)\]
InternVL scales up the ViT to _**6B parameters**_ and aligns it with LLM.
It is trained using web-scale, noisy image-text pairs. The data are all publicly available and comprise multilingual content, including LAION-en, LAION-multi, LAION-COCO, COYO, Wukong, CC12M, CC3M, and SBU.
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.
## Pretrained Weights
| model name | type | download | size |
| ----------------------- | ------- | ---------------------------------------------------------------------------------------------- | :-----: |
| InternViT-6B-224px | pytorch | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL/blob/main/intern_vit_6b_224px.pth) | 12 GB |
| InternVL-C-13B-224px | pytorch | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL/blob/main/internvl_c_13b_224px.pth) | 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](https://huggingface.co/OpenGVLab/InternVL/resolve/main/intern_vit_6b_224px_head.pth) \| [log](https://github.com/OpenGVLab/InternVL/blob/main/classification/work_dirs/intern_vit_6b_1k_224/log_rank0.txt) |
## Semantic Segmentation (ADE20K)
| type | backbone | head | mIoU | config | download |
| --------------- | --------------------- | :-----: | :--: | :--------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| few-shot (1/16) | InternViT-6B | Linear | 46.5 | [config](https://github.com/OpenGVLab/InternVL/blob/main/segmentation//configs/intern_vit_6b/few_shot/linear_intern_vit_6b_504_5k_ade20k_bs16_lr4e-5_1of16.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/linear_intern_vit_6b_504_5k_ade20k_bs16_lr4e-5_1of16.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/linear_intern_vit_6b_504_5k_ade20k_bs16_lr4e-5_1of16.log) |
| few-shot (1/8) | InternViT-6B | Linear | 50.0 | [config](https://github.com/OpenGVLab/InternVL/blob/main/segmentation//configs/intern_vit_6b/few_shot/linear_intern_vit_6b_504_10k_ade20k_bs16_lr4e-5_1of8.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/linear_intern_vit_6b_504_10k_ade20k_bs16_lr4e-5_1of8.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/linear_intern_vit_6b_504_10k_ade20k_bs16_lr4e-5_1of8.log) |
| few-shot (1/4) | InternViT-6B | Linear | 53.3 | [config](https://github.com/OpenGVLab/InternVL/blob/main/segmentation//configs/intern_vit_6b/few_shot/linear_intern_vit_6b_504_20k_ade20k_bs16_lr4e-5_1of4.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/linear_intern_vit_6b_504_20k_ade20k_bs16_lr4e-5_1of4.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/linear_intern_vit_6b_504_20k_ade20k_bs16_lr4e-5_1of4.log) |
| few-shot (1/2) | InternViT-6B | Linear | 55.8 | [config](https://github.com/OpenGVLab/InternVL/blob/main/segmentation//configs/intern_vit_6b/few_shot/linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.log) |
| few-shot (1/1) | InternViT-6B | Linear | 57.2 | [config](https://github.com/OpenGVLab/InternVL/blob/main/segmentation//configs/intern_vit_6b/few_shot/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_1of1.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_1of1.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_1of1.log) |
| linear probing | InternViT-6B (frozen) | Linear | 47.2 | [config](https://github.com/OpenGVLab/InternVL/blob/main/segmentation//configs/intern_vit_6b/linear_probing/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.log) |
| head tuning | InternViT-6B (frozen) | UperNet | 54.9 | [config](https://github.com/OpenGVLab/InternVL/blob/main/segmentation//configs/intern_vit_6b/head_tuning/upernet_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/upernet_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/upernet_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.log) |
| full tuning | InternViT-6B | UperNet | 58.9 | [config](https://github.com/OpenGVLab/InternVL/blob/main/segmentation//configs/intern_vit_6b/full_tuning/upernet_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/upernet_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/upernet_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5.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:
```BibTeX
@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](https://github.com/openai/CLIP), [Open CLIP](https://github.com/mlfoundations/open_clip), [CLIP Benchmark](https://github.com/LAION-AI/CLIP_benchmark), [EVA](https://github.com/baaivision/EVA/tree/master), [InternImage](https://github.com/OpenGVLab/InternImage), [ViT-Adapter](https://github.com/czczup/ViT-Adapter), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation), [Transformers](https://github.com/huggingface/transformers), [DINOv2](https://github.com/facebookresearch/dinov2), [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2), [Qwen-VL](https://github.com/QwenLM/Qwen-VL/tree/master/eval_mm), and [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). Thanks for their awesome work!
|