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--- |
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license: mit |
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datasets: |
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- laion/laion2B-en |
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- laion/laion-coco |
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- laion/laion2B-multi |
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- kakaobrain/coyo-700m |
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- conceptual_captions |
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- wanng/wukong100m |
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pipeline_tag: image-feature-extraction |
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--- |
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# Model Card for InternVL-14B-224px |
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<p align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/2yzk5wUY-obL6H4rKiHlU.webp" alt="Image Description" width="300" height="300"> |
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</p> |
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[\[๐ Blog\]](https://internvl.github.io/blog/) [\[๐ InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238) [\[๐ InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821) [\[๐จ๏ธ Chat Demo\]](https://internvl.opengvlab.com/) |
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[\[๐ค HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[๐ Quick Start\]](#model-usage) [\[๐ Community-hosted API\]](https://rapidapi.com/adushar1320/api/internvl-chat) [\[๐ ไธญๆ่งฃ่ฏป\]](https://zhuanlan.zhihu.com/p/675877376) |
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| Model | Date | Download | Note | |
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| ----------------------- | ---------- | ---------------------------------------------------------------------- | -------------------------------- | |
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| InternViT-6B-448px-V1-5 | 2024.04.20 | ๐ค [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) | support dynamic resolution, super strong OCR (๐ฅnew) | |
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| InternViT-6B-448px-V1-2 | 2024.02.11 | ๐ค [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2) | 448 resolution | |
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| InternViT-6B-448px-V1-0 | 2024.01.30 | ๐ค [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-0) | 448 resolution | |
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| InternViT-6B-224px | 2023.12.22 | ๐ค [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-224px) | vision foundation model | |
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| InternVL-14B-224px | 2023.12.22 | ๐ค [HF link](https://huggingface.co/OpenGVLab/InternVL-14B-224px) | vision-language foundation model | |
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## Model Details |
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- **Model Type:** vision-language foundation model |
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- **Support Tasks:** zero-shot image/video classification, image-text/video retrieval, image captioning |
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- **Model Stats:** |
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- Params: 14B |
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- Image size: 224 x 224 |
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- **Pretrain Dataset:** LAION-en, LAION-COCO, COYO, CC12M, CC3M, SBU, Wukong, LAION-multi |
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## Zero-Shot Performance |
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See this [document](https://github.com/OpenGVLab/InternVL/tree/main/clip_benchmark#-evaluation-zero-shot-image-classification) for more details about the zero-shot evaluation. |
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## Model Usage |
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**Note: the prefix `'summarize:'` and `tokenizer.pad_token_id = 0` are necessary. Their absence will lead to abnormal results.** |
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```python |
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import torch |
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from PIL import Image |
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from transformers import AutoModel, CLIPImageProcessor |
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from transformers import AutoTokenizer |
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model = AutoModel.from_pretrained( |
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'OpenGVLab/InternVL-14B-224px', |
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torch_dtype=torch.bfloat16, |
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low_cpu_mem_usage=True, |
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trust_remote_code=True).cuda().eval() |
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image_processor = CLIPImageProcessor.from_pretrained('OpenGVLab/InternVL-14B-224px') |
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tokenizer = AutoTokenizer.from_pretrained( |
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'OpenGVLab/InternVL-14B-224px', use_fast=False, add_eos_token=True) |
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tokenizer.pad_token_id = 0 # set pad_token_id to 0 |
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images = [ |
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Image.open('./examples/image1.jpg').convert('RGB'), |
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Image.open('./examples/image2.jpg').convert('RGB'), |
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Image.open('./examples/image3.jpg').convert('RGB') |
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] |
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prefix = 'summarize:' |
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texts = [ |
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prefix + 'a photo of a red panda', # English |
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prefix + 'ไธๅผ ็็ซ็็
ง็', # Chinese |
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prefix + 'ไบๅนใฎ็ซใฎๅ็' # Japanese |
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] |
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pixel_values = image_processor(images=images, return_tensors='pt').pixel_values |
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pixel_values = pixel_values.to(torch.bfloat16).cuda() |
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input_ids = tokenizer(texts, return_tensors='pt', max_length=80, |
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truncation=True, padding='max_length').input_ids.cuda() |
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# InternVL-C |
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logits_per_image, logits_per_text = model( |
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image=pixel_values, text=input_ids, mode='InternVL-C') |
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probs = logits_per_image.softmax(dim=-1) |
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# tensor([[9.9609e-01, 5.2185e-03, 6.0070e-08], |
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# [2.2949e-02, 9.7656e-01, 5.9903e-06], |
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# [3.2932e-06, 7.4863e-05, 1.0000e+00]], device='cuda:0', |
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# dtype=torch.bfloat16, grad_fn=<SoftmaxBackward0>) |
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# InternVL-G |
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logits_per_image, logits_per_text = model( |
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image=pixel_values, text=input_ids, mode='InternVL-G') |
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probs = logits_per_image.softmax(dim=-1) |
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# tensor([[9.9609e-01, 3.1738e-03, 3.6322e-08], |
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# [8.6060e-03, 9.9219e-01, 2.8759e-06], |
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# [1.7583e-06, 3.1233e-05, 1.0000e+00]], device='cuda:0', |
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# dtype=torch.bfloat16, grad_fn=<SoftmaxBackward0>) |
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# please set add_eos_token to False for generation |
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tokenizer.add_eos_token = False |
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image = Image.open('./examples/image1.jpg').convert('RGB') |
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pixel_values = image_processor(images=image, return_tensors='pt').pixel_values |
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pixel_values = pixel_values.to(torch.bfloat16).cuda() |
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tokenized = tokenizer("English caption:", return_tensors='pt') |
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pred = model.generate( |
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pixel_values=pixel_values, |
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input_ids=tokenized.input_ids.cuda(), |
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attention_mask=tokenized.attention_mask.cuda(), |
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num_beams=5, |
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min_new_tokens=8, |
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) |
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caption = tokenizer.decode(pred[0].cpu(), skip_special_tokens=True).strip() |
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# English caption: a red panda sitting on top of a wooden platform |
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``` |
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## Citation |
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If you find this project useful in your research, please consider citing: |
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```BibTeX |
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@article{chen2023internvl, |
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title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks}, |
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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}, |
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journal={arXiv preprint arXiv:2312.14238}, |
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year={2023} |
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} |
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@article{chen2024far, |
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title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites}, |
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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}, |
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journal={arXiv preprint arXiv:2404.16821}, |
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year={2024} |
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} |
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``` |
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## Acknowledgement |
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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! |
