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BMN

Introduction

[ALGORITHM]

@inproceedings{lin2019bmn,
  title={Bmn: Boundary-matching network for temporal action proposal generation},
  author={Lin, Tianwei and Liu, Xiao and Li, Xin and Ding, Errui and Wen, Shilei},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={3889--3898},
  year={2019}
}

[DATASET]

@article{zhao2017cuhk,
  title={Cuhk \& ethz \& siat submission to activitynet challenge 2017},
  author={Zhao, Y and Zhang, B and Wu, Z and Yang, S and Zhou, L and Yan, S and Wang, L and Xiong, Y and Lin, D and Qiao, Y and others},
  journal={arXiv preprint arXiv:1710.08011},
  volume={8},
  year={2017}
}

Model Zoo

ActivityNet feature

config feature gpus AR@100 AUC AP@0.5 AP@0.75 AP@0.95 mAP gpu_mem(M) iter time(s) ckpt log json
bmn_400x100_9e_2x8_activitynet_feature cuhk_mean_100 2 75.28 67.22 42.47 31.31 9.92 30.34 5420 3.27 ckpt log json
mmaction_video 2 75.43 67.22 42.62 31.56 10.86 30.77 5420 3.27 ckpt log json
mmaction_clip 2 75.35 67.38 43.08 32.19 10.73 31.15 5420 3.27 ckpt log json
BMN-official (for reference)* cuhk_mean_100 - 75.27 67.49 42.22 30.98 9.22 30.00 - - - - -
  • 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. For feature column, cuhk_mean_100 denotes the widely used cuhk activitynet feature extracted by anet2016-cuhk, mmaction_video and mmaction_clip denote feature extracted by mmaction, with video-level activitynet finetuned model or clip-level activitynet finetuned model respectively.
  3. We evaluate the action detection performance of BMN, using anet_cuhk_2017 submission for ActivityNet2017 Untrimmed Video Classification Track to assign label for each action proposal.

*We train BMN with the official repo, evaluate its proposal generation and action detection performance with anet_cuhk_2017 for label assigning.

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

Train

You can use the following command to train a model.

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

Example: train BMN model on ActivityNet features dataset.

python tools/train.py configs/localization/bmn/bmn_400x100_2x8_9e_activitynet_feature.py

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 BMN on ActivityNet feature dataset.

# Note: If evaluated, then please make sure the annotation file for test data contains groundtruth.
python tools/test.py configs/localization/bmn/bmn_400x100_2x8_9e_activitynet_feature.py checkpoints/SOME_CHECKPOINT.pth --eval AR@AN --out results.json

You can also test the action detection performance of the model, with anet_cuhk_2017 prediction file and generated proposal file (results.json in last command).

python tools/analysis/report_map.py --proposal path/to/proposal_file

Notes:

  1. (Optional) You can use the following command to generate a formatted proposal file, which will be fed into the action classifier (Currently supports SSN and P-GCN, not including TSN, I3D etc.) to get the classification result of proposals.

    python tools/data/activitynet/convert_proposal_format.py

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