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AVA

Introduction

[DATASET]

@inproceedings{gu2018ava,
  title={Ava: A video dataset of spatio-temporally localized atomic visual actions},
  author={Gu, Chunhui and Sun, Chen and Ross, David A and Vondrick, Carl and Pantofaru, Caroline and Li, Yeqing and Vijayanarasimhan, Sudheendra and Toderici, George and Ricco, Susanna and Sukthankar, Rahul and others},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={6047--6056},
  year={2018}
}

[ALGORITHM]

@article{duan2020omni,
  title={Omni-sourced Webly-supervised Learning for Video Recognition},
  author={Duan, Haodong and Zhao, Yue and Xiong, Yuanjun and Liu, Wentao and Lin, Dahua},
  journal={arXiv preprint arXiv:2003.13042},
  year={2020}
}

[ALGORITHM]

@inproceedings{feichtenhofer2019slowfast,
  title={Slowfast networks for video recognition},
  author={Feichtenhofer, Christoph and Fan, Haoqi and Malik, Jitendra and He, Kaiming},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={6202--6211},
  year={2019}
}

Model Zoo

AVA2.1

Model Modality Pretrained Backbone Input gpus Resolution mAP log json ckpt
slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb RGB Kinetics-400 ResNet50 4x16 8 short-side 256 20.1 log json ckpt
slowonly_omnisource_pretrained_r50_4x16x1_20e_ava_rgb RGB OmniSource ResNet50 4x16 8 short-side 256 21.8 log json ckpt
slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb RGB Kinetics-400 ResNet101 8x8 8x2 short-side 256 24.6 log json ckpt
slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb RGB OmniSource ResNet101 8x8 8x2 short-side 256 25.9 log json ckpt
slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb RGB Kinetics-400 ResNet50 32x2 8x2 short-side 256 24.4 log json ckpt
slowfast_context_kinetics_pretrained_r50_4x16x1_20e_ava_rgb RGB Kinetics-400 ResNet50 32x2 8x2 short-side 256 25.4 log json ckpt
slowfast_kinetics_pretrained_r50_8x8x1_20e_ava_rgb RGB Kinetics-400 ResNet50 32x2 8x2 short-side 256 25.5 log json ckpt
  • Notes:
  1. The gpus indicates the number of gpu we used to get the checkpoint. According to the Linear Scaling Rule, you may set the learning rate proportional to the batch size if you use different GPUs or videos per GPU, e.g., lr=0.01 for 4 GPUs x 2 video/gpu and lr=0.08 for 16 GPUs x 4 video/gpu.
  2. Context indicates that using both RoI feature and global pooled feature for classification, which leads to around 1% mAP improvement in general.

For more details on data preparation, you can refer to AVA in Data Preparation.

Train

You can use the following command to train a model.

python tools/train.py ${CONFIG_FILE} [optional arguments]

Example: train SlowOnly model on AVA with periodic validation.

python tools/train.py configs/detection/AVA/slowonly_kinetics_pretrained_r50_8x8x1_20e_ava_rgb.py --validate

For more details and optional arguments infos, you can refer to Training setting part in getting_started .

Test

You can use the following command to test a model.

python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]

Example: test SlowOnly model on AVA and dump the result to a csv file.

python tools/test.py configs/detection/AVA/slowonly_kinetics_pretrained_r50_8x8x1_20e_ava_rgb.py checkpoints/SOME_CHECKPOINT.pth --eval bbox --out results.csv

For more details and optional arguments infos, you can refer to Test a dataset part in getting_started .