✈️ avion

AVION is short for A VIdeo model in ONe day. AVION (meaning plane in French and Spanish) is fast.

Training a Large Video Model on a Single Machine in a Day
Yue Zhao, Philipp Krähenbühl
UT Austin
arxiv | bibtex

Installation

See INSTALL.md to install this code.

Main results

1. AVION enables video-language contrastive pre-training on Ego4D (original narratives) on a single node of 8× consumer-grade GPUs within a day.

   Method	Backbone	batch-size per GPU	GPU memory	Hardware	GPU×hour^	EK100 MIR 0-shot Avg. mAP
   EgoVLP	TSF-B	16	22	32× A100	1536	22.1
   Ours	ViT-B	256	19	8× A5000	130	27.4

   ^The reported GPU×hour is not normalized for GPU generations. The cost for EgoVLP is obtained from the original paper (Sec 6.1).

2. AVION speeds up LLM-augmented video-language contrastive pre-training (LaViLa) on Ego4D.

   a. Pretraining cost and performance.

   Method	Backbone	batch-size per GPU	GPU memory	Hardware	GPU×hour^	EK100 MIR 0-shot Avg. mAP
   LaViLa	TSF-B	32	25	32× V100	1824	30.9
   Ours	ViT-B	256	19	8× A5000	260	33.2

   ^The reported GPU×hour is not normalized for GPU generations.

   b. Downstream performance.

   Method	Backbone	EK100 MIR Avg. mAP	EK100 MIR Avg. nDCG	EK100 CLS Action Top-1
   LaViLa	TSF-B	50.5	65.0	46.9
   Ours	ViT-B	51.7	66.8	49.5
   LaViLa	TSF-L	50.9	66.5	51.0
   Ours	ViT-L	54.5	69.0	54.5

   🏆 LaViLa+AVION helps us win CVPR 2023 EPIC-Kitchens Challenges in both Action Recognition and Multi-Instance Retrieval Tasks by a significant margin.

3. AVION speeds up VideoMAE pre-training.

   Method	Backbone	Epochs	GPU×hour^^	top-1/top-5 (w/. FT)
   VideoMAE	ViT-B	800	995	80.0/94.4
   Ours	ViT-B	800	583	80.1/94.5

   ^^Both GPU×hour are measured on the same hardware environment (4× A5000 GPU).

For more details, please refer to MODEL_ZOO.

License

MIT License.

Acknowledgements

• The vision-language contrastive pretraining part is refactored from LaViLa.
• The MAE-style self-supervised pre-training part is built upon VideoMAE.

Citing AVION

@article{zhao2023training,
  title={Training a large video model on a single machine in a day},
  author={Zhao, Yue and Kr{\"a}henb{\"u}hl, Philipp},
  journal={arXiv preprint arXiv:2309.16669},
  year={2023}
}

@inproceedings{zhao2023lavila,
  title={Learning Video Representations from Large Language Models},
  author={Zhao, Yue and Misra, Ishan and Kr{\"a}henb{\"u}hl, Philipp and Girdhar, Rohit},
  booktitle={CVPR},
  year={2023}
}