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license: mit
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---
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license: mit
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datasets:
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- nlphuji/flickr30k
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# Model Card for InternVL-14B-Flickr30K-FT-364px
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## What is InternVL?
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\[[Paper](https://arxiv.org/abs/2312.14238)\] \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\]
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InternVL scales up the ViT to _**6B parameters**_ and aligns it with LLM.
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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.
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## Model Details
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- **Model Type:** fine-tuned retrieval model
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- **Support Tasks:** image-text retrieval
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- **Model Stats:**
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- Params: 14B
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- Image size: 364 x 364
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- **Finetune Dataset:** Flickr30K
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## Performance
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See this [document](https://github.com/OpenGVLab/InternVL/tree/main/internvl_g#flickr30k) for more details about the 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-Flickr30K-FT-364px',
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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-Flickr30K-FT-364px')
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tokenizer = AutoTokenizer.from_pretrained(
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'OpenGVLab/InternVL-14B-Flickr30K-FT-364px', 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 + '一张熊猫的照片', # Chinese
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prefix + '二匹の猫の写真' # 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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# 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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```
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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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```
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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!
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