Image Feature Extraction
Transformers
PyTorch
intern_vit_6b
feature-extraction
custom_code
Edit model card

Model Card for InternViT-6B-448px-V1-0

Image Description

[InternVL 1.5 Technical Report] [Paper] [GitHub] [Chat Demo] [中文解读]

We release InternViT-6B-448px-V1-0, which is integrated into InternVL-Chat-V1-1. In this update, we explored increasing the resolution to 448x448, enhancing Optical Character Recognition (OCR) capabilities, and improving support for Chinese conversations. For examples of the enhanced capabilities, please refer to the LINK.

Model Details

  • Model Type: vision foundation model, feature backbone
  • Model Stats:
    • Params (M): 5903
    • Image size: 448 x 448
  • Pretrain Dataset: LAION-en, LAION-COCO, COYO, CC12M, CC3M, SBU, Wukong, LAION-multi, OCR-related datasets.
  • Note: This model has 48 blocks, and we found that using the output after the fourth-to-last block worked best for MLLM. Therefore, when building a MLLM with this model, please use the features from the fourth-to-last layer.

Released Models

Vision Foundation model

Model Date Download Note
InternViT-6B-448px-V1.5 2024.04.20 🤗 HF link support dynamic resolution, super strong OCR (🔥new)
InternViT-6B-448px-V1.2 2024.02.11 🤗 HF link 448 resolution
InternViT-6B-448px-V1.0 2024.01.30 🤗 HF link 448 resolution
InternViT-6B-224px 2023.12.22 🤗 HF link vision foundation model
InternVL-14B-224px 2023.12.22 🤗 HF link vision-language foundation model

Multimodal Large Language Model (MLLM)

Model Date Download Note
InternVL-Chat-V1.5 2024.04.18 🤗 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. (🔥new)
InternVL-Chat-V1.2-Plus 2024.02.21 🤗 HF link more SFT data and stronger
InternVL-Chat-V1.2 2024.02.11 🤗 HF link scaling up LLM to 34B
InternVL-Chat-V1.1 2024.01.24 🤗 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-6B-448px-V1-0',
    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-448px-V1-0')

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}
}

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!

Downloads last month
526
Inference API (serverless) does not yet support model repos that contain custom code.

Datasets used to train OpenGVLab/InternViT-6B-448px-V1-0

Collection including OpenGVLab/InternViT-6B-448px-V1-0