metadata
license: mit
datasets:
- laion/laion2B-en
- laion/laion-coco
- laion/laion2B-multi
- kakaobrain/coyo-700m
- conceptual_captions
- wanng/wukong100m
pipeline_tag: image-feature-extraction
Model Card for InternViT-6B-224px
[Paper] [GitHub] [Chat Demo] [中文解读]
| 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 |
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, when building a VLLM with this model, please use the features from the fourth-to-last layer.
Linear Probing Performance
See this document 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)
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:
@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!