|
--- |
|
license: mit |
|
datasets: |
|
- laion/laion2B-en |
|
- laion/laion-coco |
|
- laion/laion2B-multi |
|
- kakaobrain/coyo-700m |
|
- conceptual_captions |
|
- wanng/wukong100m |
|
--- |
|
|
|
# Model Card for InternViT-6B-224px |
|
|
|
## What is InternVL? |
|
|
|
\[[Paper](https://arxiv.org/abs/2312.14238)\] \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\] |
|
|
|
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. |
|
|
|
 |
|
|
|
## Model Details |
|
- **Model Type:** vision foundation model, feature backbone |
|
- **Model Stats:** |
|
- Params (M): 5903 |
|
- Image size: 224 x 224 |
|
- **Pretrain Dataset:** LAION-en, LAION-COCO, COYO, CC12M, CC3M, SBU, Wukong, LAION-multi |
|
- **Note:** This model has 48 blocks, and we found that using the output after the fourth-to-last block worked best for VLLM. Therefore, **please set mm_vision_select_layer=-4 when using this model to build VLLM.** |
|
|
|
|
|
## Linear Probing Performance |
|
|
|
See this [document](https://github.com/OpenGVLab/InternVL/tree/main/classification#-evaluation) for more details about the linear probing evaluation. |
|
|
|
| IN-1K | IN-ReaL | IN-V2 | IN-A | IN-R | IN-Sketch | |
|
| :---: | :-----: | :---: | :--: | :--: | :-------: | |
|
| 88.2 | 90.4 | 79.9 | 77.5 | 89.8 | 69.1 | |
|
|
|
## Model Usage (Image Embeddings) |
|
|
|
```python |
|
import torch |
|
from PIL import Image |
|
from transformers import AutoModel, CLIPImageProcessor |
|
|
|
model = AutoModel.from_pretrained( |
|
'OpenGVLab/InternViT-6B-224px', |
|
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-6B-224px') |
|
|
|
pixel_values = image_processor(images=image, return_tensors='pt').pixel_values |
|
pixel_values = pixel_values.to(torch.bfloat16).cuda() |
|
|
|
outputs = model(pixel_values) |
|
``` |
|
|
|
## Citation |
|
|
|
If you find this project useful in your research, please consider citing: |
|
|
|
```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! |
|
|
