Model Card for InternViT-300M-448px
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This update primarily focuses on enhancing the efficiency of the vision foundation model. We developed InternViT-300M-448px by distilling knowledge from the robust vision foundation model, InternViT-6B-448px-V1-5. Like its predecessor, InternViT-300M-448px features a dynamic input resolution of 448Γ448, with a basic tile size of 448Γ448. During training, it allows for 1 to 12 tiles, and expands to 1 to 40 tiles during testing. Additionally, it inherits the powerful robustness, OCR capability, and high-resolution processing capacity from InternViT-6B-448px-V1-5.
Model Details
- Model Type: vision foundation model, feature backbone
- Model Stats:
- Params (M): 304
- Image size: 448 x 448, training with 1 - 12 tiles
- Pretrain Dataset: LAION-en, LAION-zh, COYO, GRIT, COCO, TextCaps, Objects365, OpenImages, All-Seeing, Wukong-OCR, LaionCOCO-OCR, and other OCR-related datasets. To enhance the OCR capability of the model, we have incorporated additional OCR data alongside the general caption datasets. Specifically, we utilized PaddleOCR to perform Chinese OCR on images from Wukong and English OCR on images from LAION-COCO.
Released Models
Vision Foundation model
| Model | Date | Download | Note |
|---|---|---|---|
| InternViT-6B-448px-V1-5 | 2024.04.20 | π€ HF link | support dynamic resolution, super strong OCR (π₯new) |
| InternViT-6B-448px-V1-2 | 2024.02.11 | π€ HF link | 448 resolution |
| InternViT-6B-448px-V1-0 | 2024.01.30 | π€ HF link | 448 resolution |
| InternViT-6B-224px | 2023.12.22 | π€ HF link | vision foundation model |
| InternVL-14B-224px | 2023.12.22 | π€ HF link | vision-language foundation model |
Multimodal Large Language Model (MLLM)
| Model | Date | Download | Note |
|---|---|---|---|
| InternVL-Chat-V1-5 | 2024.04.18 | π€ HF link | support 4K image; super strong OCR; Approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc. (π₯new) |
| InternVL-Chat-V1-2-Plus | 2024.02.21 | π€ HF link | more SFT data and stronger |
| InternVL-Chat-V1-2 | 2024.02.11 | π€ HF link | scaling up LLM to 34B |
| InternVL-Chat-V1-1 | 2024.01.24 | π€ HF link | support Chinese and stronger OCR |
Model Usage (Image Embeddings)
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)
Citation
If you find this project useful in your research, please consider citing:
@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}
}
@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}
}
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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