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[ECCV 2016] Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

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语言: 🇨🇳 🇺🇸

«TSN»复现了论文Temporal Segment Networks提出的视频分类模型

内容列表

背景

Temporal Segment Networks: Towards Good Practices for Deep Action Recognition是视频分类任务中的经典实现

安装

通过requirements.txt安装运行所需依赖

$ pip install -r requirements.txt

处理数据时需要额外安装denseflow,可以在innerlee/setup中找到安装脚本

使用

首先设置GPU和当前位置

$ export CUDA_VISIBLE_DEVICES=1
$ export PYTHONPATH=.
  • 训练
# 训练UCF101
# 单GPU
$ python tools/train.py --config_file=configs/tsn_r50_ucf101_rgb_224x3_seg.yaml
# 多GPU
$ python tools/train.py \
--config_file=configs/tsn_r50_ucf101_rgb_224x3_seg.yaml \
--eval_step=1000 \
--save_step=1000 \
-g=<N>
  • 测试
# 单模态测试
$ python tools/test.py <config_file> <pth_file>
$ python tools/test.py configs/tsn_r50_ucf101_rgb_224x3_seg.yaml outputs/tsn_r50_ucf101_rgb_224x3_seg.pth
# 多模态融合测试 - RGB + RGBDiff
$ python tools/fusion.py <rgb_config_file> <rgb_pth_file> <rgbdiff_config_file> <rgbdiff_pth_file>
$ python tools/fusion.py \
configs/tsn_r50_ucf101_rgb_224x3_seg.yaml \
outputs/tsn_r50_ucf101_rgb_224x3_seg.pth  \
configs/tsn_r50_ucf101_rgbdiff_224x3_seg.yaml \
outputs/tsn_r50_ucf101_rgbdiff_224x3_seg.pth

主要维护人员

  • zhujian - Initial work - zjykzj

致谢

仓库

论文

@misc{wang2016temporal,
      title={Temporal Segment Networks: Towards Good Practices for Deep Action Recognition}, 
      author={Limin Wang and Yuanjun Xiong and Zhe Wang and Yu Qiao and Dahua Lin and Xiaoou Tang and Luc Van Gool},
      year={2016},
      eprint={1608.00859},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

参与贡献方式

欢迎任何人的参与!打开issue或提交合并请求。

注意:

许可证

Apache License 2.0 © 2020 zjykzj