license: apache-2.0
datasets:
- Reself/AuroraCap-trainset
base_model:
- lmsys/vicuna-7b-v1.5-16k
tags:
- caption
model-index:
- name: AuroraCap-7B
results:
- task:
type: image caption
dataset:
type: Filckr
name: Filckr
metrics:
- type: cider
value: 88.9
- type: bleu
value: 75.6
name: bleu@1
- type: bleu
value: 32.8
name: bleu@4
- type: meteor
value: 26.7
- type: rouge
value: 55.4
name: rouge-l
- task:
type: image caption
dataset:
type: NoCaps
name: NoCaps
metrics:
- type: cider
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
- task:
type: image caption
dataset:
type: COCO-Cap
name: COCO-Cap
metrics:
- type: cider
value: 120.8
- 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
name: rouge-l
Resources
- Website
- arXiv: Paper
- GitHub: Code
- Huggingface: AuroraCap Model
- Huggingface: VDC Benchmark
- Huggingface: Trainset
Features
AuroraCap is a multimodal large language model for image and video captioning.
Quick Start
see Docs.
FAQ
Q: Can I only use token merging during inference?
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.
Q: Why do we provide both official LLaVA-format and Xtuner format weights for AuroraCap?
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.