Optimized VideoLLaMA with improved spatial-temporal modeling and better audio understanding capability โข 13 items โข Updated โข 20
VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs
If you like our project, please give us a star โญ on Github for the latest update.
๐ฐ News
- [2024.10.15] Release checkpoints of VideoLLaMA2.1-7B-16F-Base and VideoLLaMA2.1-7B-16F
- [2024.08.14] Release checkpoints of VideoLLaMA2-72B-Base and VideoLLaMA2-72B
- [2024.07.30] Release checkpoints of VideoLLaMA2-8x7B-Base and VideoLLaMA2-8x7B.
- [2024.06.25] ๐ฅ๐ฅ As of Jun 25, our VideoLLaMA2-7B-16F is the Top-1 ~7B-sized VideoLLM on the MLVU Leaderboard.
- [2024.06.18] ๐ฅ๐ฅ As of Jun 18, our VideoLLaMA2-7B-16F is the Top-1 ~7B-sized VideoLLM on the VideoMME Leaderboard.
- [2024.06.17] ๐๐ Update technical report with the latest results and the missing references. If you have works closely related to VideoLLaMA 2 but not mentioned in the paper, feel free to let us know.
- [2024.06.14] ๐ฅ๐ฅ Online Demo is available.
- [2024.06.03] Release training, evaluation, and serving codes of VideoLLaMA 2.
๐ Model Zoo
| Model Name | Type | Visual Encoder | Language Decoder | # Training Frames |
|---|---|---|---|---|
| VideoLLaMA2-7B-Base | Base | clip-vit-large-patch14-336 | Mistral-7B-Instruct-v0.2 | 8 |
| VideoLLaMA2-7B | Chat | clip-vit-large-patch14-336 | Mistral-7B-Instruct-v0.2 | 8 |
| VideoLLaMA2-7B-16F-Base | Base | clip-vit-large-patch14-336 | Mistral-7B-Instruct-v0.2 | 16 |
| VideoLLaMA2-7B-16F | Chat | clip-vit-large-patch14-336 | Mistral-7B-Instruct-v0.2 | 16 |
| VideoLLaMA2-8x7B-Base | Base | clip-vit-large-patch14-336 | Mixtral-8x7B-Instruct-v0.1 | 8 |
| VideoLLaMA2-8x7B | Chat | clip-vit-large-patch14-336 | Mixtral-8x7B-Instruct-v0.1 | 8 |
| VideoLLaMA2-72B-Base | Base | clip-vit-large-patch14-336 | Qwen2-72B-Instruct | 8 |
| VideoLLaMA2-72B | Chat | clip-vit-large-patch14-336 | Qwen2-72B-Instruct | 8 |
| VideoLLaMA2.1-7B-16F-Base (This Checkpoint) | Base | siglip-so400m-patch14-384 | Qwen2-7B-Instruct | 16 |
| VideoLLaMA2.1-7B-16F | Chat | siglip-so400m-patch14-384 | Qwen2-7B-Instruct | 16 |
๐ Main Results
Multi-Choice Video QA & Video Captioning
Open-Ended Video QA
๐ค Inference with VideoLLaMA2
import sys
sys.path.append('./')
from videollama2 import model_init, mm_infer
from videollama2.utils import disable_torch_init
def inference():
disable_torch_init()
# Video Inference
modal = 'video'
modal_path = 'assets/cat_and_chicken.mp4'
instruct = 'What animals are in the video, what are they doing, and how does the video feel?'
# Image Inference
modal = 'image'
modal_path = 'assets/sora.png'
instruct = 'What is the woman wearing, what is she doing, and how does the image feel?'
model_path = 'DAMO-NLP-SG/VideoLLaMA2-7B-16F'
model, processor, tokenizer = model_init(model_path)
output = mm_infer(processor[modal](modal_path), instruct, model=model, tokenizer=tokenizer, do_sample=False, modal=modal)
print(output)
if __name__ == "__main__":
inference()
Citation
If you find VideoLLaMA useful for your research and applications, please cite using this BibTeX:
@article{damonlpsg2024videollama2,
title={VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs},
author={Cheng, Zesen and Leng, Sicong and Zhang, Hang and Xin, Yifei and Li, Xin and Chen, Guanzheng and Zhu, Yongxin and Zhang, Wenqi and Luo, Ziyang and Zhao, Deli and Bing, Lidong},
journal={arXiv preprint arXiv:2406.07476},
year={2024},
url = {https://arxiv.org/abs/2406.07476}
}
@article{damonlpsg2023videollama,
title = {Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding},
author = {Zhang, Hang and Li, Xin and Bing, Lidong},
journal = {arXiv preprint arXiv:2306.02858},
year = {2023},
url = {https://arxiv.org/abs/2306.02858}
}
- Downloads last month
- 4
