README.md

BMN

Introduction

@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}
}

Model Zoo

ActivityNet feature

config	feature	gpus	pretrain	AR@100	AUC	gpu_mem(M)	iter time(s)	ckpt	log	json
bmn_400x100_9e_2x8_activitynet_feature	cuhk_mean_100	2	None	75.28	67.22	5420	3.27	ckpt	log	json
	mmaction_video	2	None	75.43	67.22	5420	3.27	ckpt	log	json
	mmaction_clip	2	None	75.35	67.38	5420	3.27	ckpt	log	json

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 * 2 video/gpu and lr=0.08 for 16 GPUs * 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.

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

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