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--- |
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license: apache-2.0 |
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datasets: |
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- wchai/AuroraCap-trainset |
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base_model: |
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- lmsys/vicuna-7b-v1.5-16k |
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tags: |
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- caption |
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model-index: |
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- name: AuroraCap-7B |
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results: |
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- task: |
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type: image caption |
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dataset: |
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type: Filckr |
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name: Filckr |
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metrics: |
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- type: cider |
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value: 88.9 |
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- type: bleu |
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value: 75.6 |
|
name: bleu@1 |
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- type: bleu |
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value: 32.8 |
|
name: bleu@4 |
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- type: meteor |
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value: 26.7 |
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- type: rouge |
|
value: 55.4 |
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name: rouge-l |
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- task: |
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type: image caption |
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dataset: |
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type: NoCaps |
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name: NoCaps |
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metrics: |
|
- type: cider |
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value: 111.4 |
|
- type: bleu |
|
value: 85.6 |
|
name: bleu@1 |
|
- type: bleu |
|
value: 44.4 |
|
name: bleu@4 |
|
- type: meteor |
|
value: 29.9 |
|
- type: rouge |
|
value: 60.6 |
|
name: rouge-l |
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- task: |
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type: image caption |
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dataset: |
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type: COCO-Cap |
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name: COCO-Cap |
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metrics: |
|
- type: cider |
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value: 120.8 |
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- type: bleu |
|
value: 78 |
|
name: bleu@1 |
|
- type: bleu |
|
value: 35.3 |
|
name: bleu@4 |
|
- type: meteor |
|
value: 28.6 |
|
- type: rouge |
|
value: 57.2 |
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name: rouge-l |
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pipeline_tag: image-text-to-text |
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--- |
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|
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<img src="assets/teaser.png" align="center"> |
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|
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## Resources |
|
|
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- [Website](https://rese1f.github.io/aurora-web/) |
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- [arXiv: Paper](https://arxiv.org/abs/2410.03051) |
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- [GitHub: Code](https://github.com/rese1f/aurora) |
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- [Huggingface: AuroraCap Model](https://huggingface.co/collections/Reself/auroracap-66d117ffe13bedda96702013) |
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- [Huggingface: VDC Benchmark](https://huggingface.co/datasets/Reself/Video-Detailed-Caption) |
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- [Huggingface: Trainset](https://huggingface.co/datasets/Reself/AuroraCap-trainset) |
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|
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## Features |
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|
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<img src="assets/vdc_baseline.png" align="center"> |
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|
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AuroraCap is a multimodal large language model for image and video captioning. |
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|
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## Quick Start |
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|
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see [Docs](https://github.com/rese1f/aurora/blob/main/docs/auroracap/README.md). |
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|
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## FAQ |
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|
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Q: Can I only use token merging during inference? |
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|
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A: No, our experiments show that token merging is also a way to accelerate training while maintaining similar performance. Additionally, besides auroracap, you can also use token merging on other llava-like models. |
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|
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Q: Why do we provide both official LLaVA-format and Xtuner format weights for AuroraCap? |
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|
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A: While Xtuner supports saving checkpoints in multiple formats, it currently only allows continued training with the Xtuner format. Therefore, we currently provide the model in the Xtuner format for both continued training and inference. In the future, we will provide the model in the official LLaVA format for both training and inference, enabling quicker SGLang deployment and integration with the transformers. |
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|
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## Citation |
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|
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``` |
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@article{chai2024auroracap, |
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title={AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark }, |
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author={Wenhao Chai, Enxin Song, Yilun Du, Chenlin Meng, Vashisht Madhavan, Omer Bar-Tal, Jeng-Neng Hwang, Saining Xie, Christopher D. Manning}, |
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journal={arXiv preprint arXiv:2410.03051}, |
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year={2024} |
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} |
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``` |
