# TSN ## 简介 ```BibTeX @inproceedings{wang2016temporal, title={Temporal segment networks: Towards good practices for deep action recognition}, author={Wang, Limin and Xiong, Yuanjun and Wang, Zhe and Qiao, Yu and Lin, Dahua and Tang, Xiaoou and Van Gool, Luc}, booktitle={European conference on computer vision}, pages={20--36}, year={2016}, organization={Springer} } ``` ## 模型库 ### UCF-101 | 配置文件 | GPU 数量 | 主干网络 | 预训练 | top1 准确率 | top5 准确率 | GPU 显存占用 (M) | ckpt | log | json | | :--------------------------------------------------------------------------------------------- | :------: | :------: | :------: | :---------: | :---------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------: | | [tsn_r50_1x1x3_75e_ucf101_rgb](/configs/recognition/tsn/tsn_r50_1x1x3_75e_ucf101_rgb.py) \[1\] | 8 | ResNet50 | ImageNet | 83.03 | 96.78 | 8332 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_75e_ucf101_rgb/tsn_r50_1x1x3_75e_ucf101_rgb_20201023-d85ab600.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_75e_ucf101_rgb/tsn_r50_1x1x3_75e_ucf101_rgb_20201023.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_75e_ucf101_rgb/tsn_r50_1x1x3_75e_ucf101_rgb_20201023.json) | \[1\] 这里汇报的是 UCF-101 的 split1 部分的结果。 ### Diving48 | 配置文件 | GPU 数量 | 主干网络 | 预训练 | top1 准确率 | top5 准确率 | GPU 显存占用 (M) | ckpt | log | json | | :----------------------------------------------------------------------------------------------------------- | :------: | :------: | :------: | :---------: | :---------: | :--------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------: | | [tsn_r50_video_1x1x8_100e_diving48_rgb](/configs/recognition/tsn/tsn_r50_video_1x1x8_100e_diving48_rgb.py) | 8 | ResNet50 | ImageNet | 71.27 | 95.74 | 5699 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x8_100e_diving48_rgb/tsn_r50_video_1x1x8_100e_diving48_rgb_20210426-6dde0185.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x8_100e_diving48_rgb/20210426_014138.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x8_100e_diving48_rgb/20210426_014138.log.json) | | [tsn_r50_video_1x1x16_100e_diving48_rgb](/configs/recognition/tsn/tsn_r50_video_1x1x16_100e_diving48_rgb.py) | 8 | ResNet50 | ImageNet | 76.75 | 96.95 | 5705 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x16_100e_diving48_rgb/tsn_r50_video_1x1x16_100e_diving48_rgb_20210426-63c5f2f7.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x16_100e_diving48_rgb/20210426_014103.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x16_100e_diving48_rgb/20210426_014103.log.json) | ### HMDB51 | 配置文件 | GPU 数量 | 主干网络 | 预训练 | top1 准确率 | top5 准确率 | GPU 显存占用 (M) | ckpt | log | json | | :--------------------------------------------------------------------------------------------------------------- | :------: | :------: | :---------: | :---------: | :---------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------: | | [tsn_r50_1x1x8_50e_hmdb51_imagenet_rgb](/configs/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_imagenet_rgb.py) | 8 | ResNet50 | ImageNet | 48.95 | 80.19 | 21535 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_imagenet_rgb/tsn_r50_1x1x8_50e_hmdb51_imagenet_rgb_20201123-ce6c27ed.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_imagenet_rgb/20201025_231108.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_imagenet_rgb/20201025_231108.log.json) | | [tsn_r50_1x1x8_50e_hmdb51_kinetics400_rgb](/configs/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_kinetics400_rgb.py) | 8 | ResNet50 | Kinetics400 | 56.08 | 84.31 | 21535 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_kinetics400_rgb/tsn_r50_1x1x8_50e_hmdb51_kinetics400_rgb_20201123-7f84701b.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_kinetics400_rgb/20201108_190805.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_kinetics400_rgb/20201108_190805.log.json) | | [tsn_r50_1x1x8_50e_hmdb51_mit_rgb](/configs/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_mit_rgb.py) | 8 | ResNet50 | Moments | 54.25 | 83.86 | 21535 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_mit_rgb/tsn_r50_1x1x8_50e_hmdb51_mit_rgb_20201123-01526d41.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_mit_rgb/20201112_170135.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_mit_rgb/20201112_170135.log.json) | ### Kinetics-400 | 配置文件 | 分辨率 | GPU 数量 | 主干网络 | 预训练 | top1 准确率 | top5 准确率 | 参考代码的 top1 准确率 | 参考代码的 top5 准确率 | 推理时间 (video/s) | GPU 显存占用 (M) | ckpt | log | json | | :--------------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :------: | :------: | :---------: | :---------: | :------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------: | :----------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | [tsn_r50_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py) | 340x256 | 8 | ResNet50 | ImageNet | 70.60 | 89.26 | x | x | 4.3 (25x10 frames) | 8344 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/20200614_063526.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/20200614_063526.log.json) | | [tsn_r50_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py) | 短边 256 | 8 | ResNet50 | ImageNet | 70.42 | 89.03 | x | x | x | 8343 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_256p_1x1x3_100e_kinetics400_rgb/tsn_r50_256p_1x1x3_100e_kinetics400_rgb_20200725-22592236.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_256p_1x1x3_100e_kinetics400_rgb/20200725_031325.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_256p_1x1x3_100e_kinetics400_rgb/20200725_031325.log.json) | | [tsn_r50_dense_1x1x5_50e_kinetics400_rgb](/configs/recognition/tsn/tsn_r50_dense_1x1x5_100e_kinetics400_rgb.py) | 340x256 | 8x3 | ResNet50 | ImageNet | 70.18 | 89.10 | [69.15](https://github.com/mit-han-lab/temporal-shift-module/tree/8d53d6fda40bea2f1b37a6095279c4b454d672bd#training) | [88.56](https://github.com/mit-han-lab/temporal-shift-module/tree/8d53d6fda40bea2f1b37a6095279c4b454d672bd#training) | 12.7 (8x10 frames) | 7028 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_dense_1x1x5_100e_kinetics400_rgb/tsn_r50_dense_1x1x5_100e_kinetics400_rgb_20200627-a063165f.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_dense_1x1x5_100e_kinetics400_rgb/20200627_105310.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_dense_1x1x5_100e_kinetics400_rgb/20200627_105310.log.json) | | [tsn_r50_320p_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/tsn_r50_320p_1x1x3_100e_kinetics400_rgb.py) | 短边 320 | 8x2 | ResNet50 | ImageNet | 70.91 | 89.51 | x | x | 10.7 (25x3 frames) | 8344 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x3_100e_kinetics400_rgb/tsn_r50_320p_1x1x3_100e_kinetics400_rgb_20200702-cc665e2a.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x3_100e_kinetics400_rgb/tsn_r50_f3_kinetics400_shortedge_70.9_89.5.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x3_100e_kinetics400_rgb/tsn_r50_f3_kinetics400_shortedge_70.9_89.5.log.json) | | [tsn_r50_320p_1x1x3_110e_kinetics400_flow](/configs/recognition/tsn/tsn_r50_320p_1x1x3_110e_kinetics400_flow.py) | 短边 320 | 8x2 | ResNet50 | ImageNet | 55.70 | 79.85 | x | x | x | 8471 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x3_110e_kinetics400_flow/tsn_r50_320p_1x1x3_110e_kinetics400_flow_20200705-3036bab6.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x3_110e_kinetics400_flow/tsn_r50_f3_kinetics400_flow_shortedge_55.7_79.9.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x3_110e_kinetics400_flow/tsn_r50_f3_kinetics400_flow_shortedge_55.7_79.9.log.json) | | tsn_r50_320p_1x1x3_kinetics400_twostream \[1: 1\]\* | x | x | ResNet50 | ImageNet | 72.76 | 90.52 | x | x | x | x | x | x | x | | [tsn_r50_1x1x8_100e_kinetics400_rgb](/configs/recognition/tsn/tsn_r50_320p_1x1x8_100e_kinetics400_rgb.py) | 短边 256 | 8 | ResNet50 | ImageNet | 71.80 | 90.17 | x | x | x | 8343 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_256p_1x1x8_100e_kinetics400_rgb/tsn_r50_256p_1x1x8_100e_kinetics400_rgb_20200817-883baf16.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_256p_1x1x8_100e_kinetics400_rgb/20200815_173413.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_256p_1x1x8_100e_kinetics400_rgb/20200815_173413.log.json) | | [tsn_r50_320p_1x1x8_100e_kinetics400_rgb](/configs/recognition/tsn/tsn_r50_320p_1x1x8_100e_kinetics400_rgb.py) | 短边 320 | 8x3 | ResNet50 | ImageNet | 72.41 | 90.55 | x | x | 11.1 (25x3 frames) | 8344 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_100e_kinetics400_rgb/tsn_r50_320p_1x1x8_100e_kinetics400_rgb_20200702-ef80e3d7.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_100e_kinetics400_rgb/tsn_r50_f8_kinetics400_shortedge_72.4_90.6.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_100e_kinetics400_rgb/tsn_r50_f8_kinetics400_shortedge_72.4_90.6.log.json) | | [tsn_r50_320p_1x1x8_110e_kinetics400_flow](/configs/recognition/tsn/tsn_r50_320p_1x1x8_110e_kinetics400_flow.py) | 短边 320 | 8x4 | ResNet50 | ImageNet | 57.76 | 80.99 | x | x | x | 8473 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_110e_kinetics400_flow/tsn_r50_320p_1x1x8_110e_kinetics400_flow_20200705-1f39486b.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_110e_kinetics400_flow/tsn_r50_f8_kinetics400_flow_shortedge_57.8_81.0.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_110e_kinetics400_flow/tsn_r50_f8_kinetics400_flow_shortedge_57.8_81.0.log.json) | | tsn_r50_320p_1x1x8_kinetics400_twostream \[1: 1\]\* | x | x | ResNet50 | ImageNet | 74.64 | 91.77 | x | x | x | x | x | x | x | | [tsn_r50_video_320p_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/tsn_r50_video_320p_1x1x3_100e_kinetics400_rgb.py) | 短边 320 | 8 | ResNet50 | ImageNet | 71.11 | 90.04 | x | x | x | 8343 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_320p_1x1x3_100e_kinetics400_rgb/tsn_r50_video_320p_1x1x3_100e_kinetics400_rgb_20201014-5ae1ee79.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_320p_1x1x3_100e_kinetics400_rgb/tsn_r50_video_320p_1x1x3_100e_kinetics400_rgb_20201014.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_320p_1x1x3_100e_kinetics400_rgb/tsn_r50_video_320p_1x1x3_100e_kinetics400_rgb_20201014.json) | | [tsn_r50_dense_1x1x8_100e_kinetics400_rgb](/configs/recognition/tsn/tsn_r50_dense_1x1x8_100e_kinetics400_rgb.py) | 340x256 | 8 | ResNet50 | ImageNet | 70.77 | 89.3 | [68.75](https://github.com/mit-han-lab/temporal-shift-module/tree/8d53d6fda40bea2f1b37a6095279c4b454d672bd#training) | [88.42](https://github.com/mit-han-lab/temporal-shift-module/tree/8d53d6fda40bea2f1b37a6095279c4b454d672bd#training) | 12.2 (8x10 frames) | 8344 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_dense_1x1x8_100e_kinetics400_rgb/tsn_r50_dense_1x1x8_100e_kinetics400_rgb_20200606-e925e6e3.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_dense_1x1x8_100e_kinetics400_rgb/20200606_003901.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_dense_1x1x8_100e_kinetics400_rgb/20200606_003901.log.json) | | [tsn_r50_video_1x1x8_100e_kinetics400_rgb](/configs/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics400_rgb.py) | 短边 256 | 8 | ResNet50 | ImageNet | 71.14 | 89.63 | x | x | x | 21558 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics400_rgb/tsn_r50_video_1x1x8_100e_kinetics400_rgb_20200702-568cde33.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics400_rgb/tsn_r50_video_2d_1x1x8_100e_kinetics400_rgb.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics400_rgb/tsn_r50_video_2d_1x1x8_100e_kinetics400_rgb.log.json) | | [tsn_r50_video_dense_1x1x8_100e_kinetics400_rgb](/configs/recognition/tsn/tsn_r50_video_dense_1x1x8_100e_kinetics400_rgb.py) | 短边 256 | 8 | ResNet50 | ImageNet | 70.40 | 89.12 | x | x | x | 21553 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_dense_1x1x8_100e_kinetics400_rgb/tsn_r50_video_dense_1x1x8_100e_kinetics400_rgb_20200703-0f19175f.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_dense_1x1x8_100e_kinetics400_rgb/tsn_r50_video_2d_1x1x8_dense_100e_kinetics400_rgb.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_dense_1x1x8_100e_kinetics400_rgb/tsn_r50_video_2d_1x1x8_dense_100e_kinetics400_rgb.log.json) | 这里，MMAction2 使用 \[1: 1\] 表示以 1: 1 的比例融合 RGB 和光流两分支的融合结果（融合前不经过 softmax） ### 在 TSN 模型中使用第三方的主干网络 用户可在 MMAction2 的框架中使用第三方的主干网络训练 TSN，例如： - [x] MMClassification 中的主干网络 - [x] TorchVision 中的主干网络 - [x] pytorch-image-models(timm) 中的主干网络 | 配置文件 | 分辨率 | GPU 数量 | 主干网络 | 预训练 | top1 准确率 | top5 准确率 | ckpt | log | json | | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------: | :------: | :------------------------------------------------------------------------------------------------------: | :------: | :---------: | :---------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | [tsn_rn101_32x4d_320p_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/custom_backbones/tsn_rn101_32x4d_320p_1x1x3_100e_kinetics400_rgb.py) | 短边 320 | 8x2 | ResNeXt101-32x4d \[[MMCls](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext)\] | ImageNet | 73.43 | 91.01 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/custom_backbones/tsn_rn101_32x4d_320p_1x1x3_100e_kinetics400_rgb-16a8b561.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/custom_backbones/tsn_rn101_32x4d_320p_1x1x3_100e_kinetics400_rgb.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/custom_backbones/tsn_rn101_32x4d_320p_1x1x3_100e_kinetics400_rgb.json) | | [tsn_dense161_320p_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/custom_backbones/tsn_dense161_320p_1x1x3_100e_kinetics400_rgb.py) | 短边 320 | 8x2 | Densenet-161 \[[TorchVision](https://github.com/pytorch/vision/)\] | ImageNet | 72.78 | 90.75 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/custom_backbones/tsn_dense161_320p_1x1x3_100e_kinetics400_rgb/tsn_dense161_320p_1x1x3_100e_kinetics400_rgb-cbe85332.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/custom_backbones/tsn_dense161_320p_1x1x3_100e_kinetics400_rgb/tsn_dense161_320p_1x1x3_100e_kinetics400_rgb.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/custom_backbones/tsn_dense161_320p_1x1x3_100e_kinetics400_rgb/tsn_dense161_320p_1x1x3_100e_kinetics400_rgb.json) | | [tsn_swin_transformer_video_320p_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/custom_backbones/tsn_swin_transformer_video_320p_1x1x3_100e_kinetics400_rgb.py) | short-side 320 | 8 | Swin Transformer Base \[[timm](https://github.com/rwightman/pytorch-image-models)\] | ImageNet | 77.51 | 92.92 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/custom_backbones/tsn_swin_transformer_video_320p_1x1x3_100e_kinetics400_rgb/tsn_swin_transformer_video_320p_1x1x3_100e_kinetics400_rgb-805380f6.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/custom_backbones/tsn_swin_transformer_video_320p_1x1x3_100e_kinetics400_rgb/tsn_swin_transformer_video_320p_1x1x3_100e_kinetics400_rgb.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/custom_backbones/tsn_swin_transformer_video_320p_1x1x3_100e_kinetics400_rgb/tsn_swin_transformer_video_320p_1x1x3_100e_kinetics400_rgb.json) | 1. 由于多种原因，TIMM 中的一些模型未能收到支持，详情请参考 [PR #880](https://github.com/open-mmlab/mmaction2/pull/880)。 ### Kinetics-400 数据基准测试 (8 块 GPU, ResNet50, ImageNet 预训练; 3 个视频段) 在数据基准测试中，比较： 1. 不同的数据预处理方法：(1) 视频分辨率为 340x256, (2) 视频分辨率为短边 320px, (3) 视频分辨率为短边 256px; 2. 不同的数据增强方法：(1) MultiScaleCrop, (2) RandomResizedCrop; 3. 不同的测试方法：(1) 25 帧 x 10 裁剪片段, (2) 25 frames x 3 裁剪片段. | 配置文件 | 分辨率 | 训练时的数据增强 | 测试时的策略 | top1 准确率 | top5 准确率 | ckpt | log | json | | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------: | :---------------: | :----------: | :---------: | :---------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | [tsn_r50_multiscalecrop_340x256_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/data_benchmark/tsn_r50_multiscalecrop_340x256_1x1x3_100e_kinetics400_rgb.py) | 340x256 | MultiScaleCrop | 25x10 frames | 70.60 | 89.26 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/20200614_063526.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/20200614_063526.log.json) | | x | 340x256 | MultiScaleCrop | 25x3 frames | 70.52 | 89.39 | x | x | x | | [tsn_r50_randomresizedcrop_340x256_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/data_benchmark/tsn_r50_randomresizedcrop_340x256_1x1x3_100e_kinetics400_rgb.py) | 340x256 | RandomResizedCrop | 25x10 frames | 70.11 | 89.01 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/data_benchmark/tsn_r50_randomresizedcrop_340x256_1x1x3_100e_kinetics400_rgb/tsn_r50_randomresizedcrop_340x256_1x1x3_100e_kinetics400_rgb_20200725-88cb325a.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/data_benchmark/tsn_r50_randomresizedcrop_340x256_1x1x3_100e_kinetics400_rgb/tsn_r50_randomresizedcrop_340x256_1x1x3_100e_kinetics400_rgb_20200725.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/data_benchmark/tsn_r50_randomresizedcrop_340x256_1x1x3_100e_kinetics400_rgb/tsn_r50_randomresizedcrop_340x256_1x1x3_100e_kinetics400_rgb_20200725.json) | | x | 340x256 | RandomResizedCrop | 25x3 frames | 69.95 | 89.02 | x | x | x | | [tsn_r50_multiscalecrop_320p_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/data_benchmark/tsn_r50_multiscalecrop_320p_1x1x3_100e_kinetics400_rgb.py) | 短边 320 | MultiScaleCrop | 25x10 frames | 70.32 | 89.25 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/data_benchmark/tsn_r50_multiscalecrop_320p_1x1x3_100e_kinetics400_rgb/tsn_r50_multiscalecrop_320p_1x1x3_100e_kinetics400_rgb_20200725-9922802f.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/data_benchmark/tsn_r50_multiscalecrop_320p_1x1x3_100e_kinetics400_rgb/tsn_r50_multiscalecrop_320p_1x1x3_100e_kinetics400_rgb_20200725.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/data_benchmark/tsn_r50_multiscalecrop_320p_1x1x3_100e_kinetics400_rgb/tsn_r50_multiscalecrop_320p_1x1x3_100e_kinetics400_rgb_20200725.json) | | x | 短边 320 | MultiScaleCrop | 25x3 frames | 70.54 | 89.39 | x | x | x | | [tsn_r50_randomresizedcrop_320p_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/data_benchmark/tsn_r50_randomresizedcrop_320p_1x1x3_100e_kinetics400_rgb.py) | 短边 320 | RandomResizedCrop | 25x10 frames | 70.44 | 89.23 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x3_100e_kinetics400_rgb/tsn_r50_320p_1x1x3_100e_kinetics400_rgb_20200702-cc665e2a.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x3_100e_kinetics400_rgb/tsn_r50_f3_kinetics400_shortedge_70.9_89.5.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x3_100e_kinetics400_rgb/tsn_r50_f3_kinetics400_shortedge_70.9_89.5.log.json) | | x | 短边 320 | RandomResizedCrop | 25x3 frames | 70.91 | 89.51 | x | x | x | | [tsn_r50_multiscalecrop_256p_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/data_benchmark/tsn_r50_multiscalecrop_256p_1x1x3_100e_kinetics400_rgb.py) | 短边 256 | MultiScaleCrop | 25x10 frames | 70.42 | 89.03 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_256p_1x1x3_100e_kinetics400_rgb/tsn_r50_256p_1x1x3_100e_kinetics400_rgb_20200725-22592236.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_256p_1x1x3_100e_kinetics400_rgb/20200725_031325.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_256p_1x1x3_100e_kinetics400_rgb/20200725_031325.log.json) | | x | 短边 256 | MultiScaleCrop | 25x3 frames | 70.79 | 89.42 | x | x | x | | [tsn_r50_randomresizedcrop_256p_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/data_benchmark/tsn_r50_randomresizedcrop_256p_1x1x3_100e_kinetics400_rgb.py) | 短边 256 | RandomResizedCrop | 25x10 frames | 69.80 | 89.06 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_256p_randomresize_1x1x3_100e_kinetics400_rgb/tsn_r50_256p_randomresize_1x1x3_100e_kinetics400_rgb_20200817-ae7963ca.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_256p_randomresize_1x1x3_100e_kinetics400_rgb/20200815_172601.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_256p_randomresize_1x1x3_100e_kinetics400_rgb/20200815_172601.log.json) | | x | 短边 256 | RandomResizedCrop | 25x3 frames | 70.48 | 89.89 | x | x | x | ### Kinetics-400 OmniSource 实验 | 配置文件 | 分辨率 | 主干网络 | 预训练 | w. OmniSource | top1 准确率 | top5 准确率 | 推理时间 (video/s) | GPU 显存占用 (M) | ckpt | log | json | | :--------------------------------------------------------------------------------------------------: | :------: | :------: | :---------: | :----------------: | :---------: | :---------: | :----------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | | [tsn_r50_1x1x3_100e_kinetics400_rgb](/configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py) | 340x256 | ResNet50 | ImageNet | :x: | 70.6 | 89.3 | 4.3 (25x10 frames) | 8344 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/20200614_063526.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/20200614_063526.log.json) | | x | 340x256 | ResNet50 | ImageNet | :heavy_check_mark: | 73.6 | 91.0 | x | 8344 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/omni/tsn_imagenet_pretrained_r50_omni_1x1x3_kinetics400_rgb_20200926-54192355.pth) | x | x | | x | 短边 320 | ResNet50 | IG-1B \[1\] | :x: | 73.1 | 90.4 | x | 8344 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/omni/tsn_1G1B_pretrained_r50_without_omni_1x1x3_kinetics400_rgb_20200926-c133dd49.pth) | x | x | | x | 短边 320 | ResNet50 | IG-1B \[1\] | :heavy_check_mark: | 75.7 | 91.9 | x | 8344 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/omni/tsn_1G1B_pretrained_r50_omni_1x1x3_kinetics400_rgb_20200926-2863fed0.pth) | x | x | \[1\] MMAction2 使用 [torch-hub](https://pytorch.org/hub/facebookresearch_semi-supervised-ImageNet1K-models_resnext/) 提供的 `resnet50_swsl` 预训练模型。 ### Kinetics-600 | 配置文件 | 分辨率 | GPU 数量 | 主干网络 | 预训练 | top1 准确率 | top5 准确率 | 推理时间 (video/s) | GPU 显存占用 (M) | ckpt | log | json | | :--------------------------------------------------------------------------------------------------------------- | :------: | :------: | :------: | :------: | :---------: | :---------: | :----------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | | [tsn_r50_video_1x1x8_100e_kinetics600_rgb](/configs/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics600_rgb.py) | 短边 256 | 8x2 | ResNet50 | ImageNet | 74.8 | 92.3 | 11.1 (25x3 frames) | 8344 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics600_rgb/tsn_r50_video_1x1x8_100e_kinetics600_rgb_20201015-4db3c461.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics600_rgb/tsn_r50_video_1x1x8_100e_kinetics600_rgb_20201015.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics600_rgb/tsn_r50_video_1x1x8_100e_kinetics600_rgb_20201015.json) | ### Kinetics-700 | 配置文件 | 分辨率 | GPU 数量 | 主干网络 | 预训练 | top1 准确率 | top5 准确率 | 推理时间 (video/s) | GPU 显存占用 (M) | ckpt | log | json | | :--------------------------------------------------------------------------------------------------------------- | :------: | :------: | :------: | :------: | :---------: | :---------: | :----------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | | [tsn_r50_video_1x1x8_100e_kinetics700_rgb](/configs/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics700_rgb.py) | 短边 256 | 8x2 | ResNet50 | ImageNet | 61.7 | 83.6 | 11.1 (25x3 frames) | 8344 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics700_rgb/tsn_r50_video_1x1x8_100e_kinetics700_rgb_20201015-e381a6c7.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics700_rgb/tsn_r50_video_1x1x8_100e_kinetics700_rgb_20201015.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics700_rgb/tsn_r50_video_1x1x8_100e_kinetics700_rgb_20201015.json) | ### Something-Something V1 | 配置文件 | 分辨率 | GPU 数量 | 主干网络 | 预训练 | top1 准确率 | top5 准确率 | 参考代码的 top1 准确率 | 参考代码的 top5 准确率 | GPU 显存占用 (M) | ckpt | log | json | | :--------------------------------------------------------------------------------------- | :----: | :------: | :------: | :------: | :---------: | :---------: | :------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------: | | [tsn_r50_1x1x8_50e_sthv1_rgb](/configs/recognition/tsn/tsn_r50_1x1x8_50e_sthv1_rgb.py) | 高 100 | 8 | ResNet50 | ImageNet | 18.55 | 44.80 | [17.53](https://github.com/mit-han-lab/temporal-shift-module/tree/8d53d6fda40bea2f1b37a6095279c4b454d672bd#training) | [44.29](https://github.com/mit-han-lab/temporal-shift-module/tree/8d53d6fda40bea2f1b37a6095279c4b454d672bd#training) | 10978 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_sthv1_rgb/tsn_r50_1x1x8_50e_sthv1_rgb_20200618-061b9195.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_sthv1_rgb/tsn_sthv1.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_sthv1_rgb/tsn_r50_f8_sthv1_18.1_45.0.log.json) | | [tsn_r50_1x1x16_50e_sthv1_rgb](/configs/recognition/tsn/tsn_r50_1x1x16_50e_sthv1_rgb.py) | 高 100 | 8 | ResNet50 | ImageNet | 15.77 | 39.85 | [13.33](https://github.com/mit-han-lab/temporal-shift-module/tree/8d53d6fda40bea2f1b37a6095279c4b454d672bd#training) | [35.58](https://github.com/mit-han-lab/temporal-shift-module/tree/8d53d6fda40bea2f1b37a6095279c4b454d672bd#training) | 5691 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x16_50e_sthv1_rgb/tsn_r50_1x1x16_50e_sthv1_rgb_20200614-7e2fe4f1.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x16_50e_sthv1_rgb/20200614_211932.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x16_50e_sthv1_rgb/20200614_211932.log.json) | ### Something-Something V2 | 配置文件 | 分辨率 | GPU 数量 | 主干网络 | 预训练 | top1 准确率 | top5 准确率 | 参考代码的 top1 准确率 | 参考代码的 top5 准确率 | GPU 显存占用 (M) | ckpt | log | json | | :--------------------------------------------------------------------------------------- | :----: | :------: | :------: | :------: | :---------: | :---------: | :--------------------: | :--------------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | | [tsn_r50_1x1x8_50e_sthv2_rgb](/configs/recognition/tsn/tsn_r50_1x1x8_50e_sthv2_rgb.py) | 高 256 | 8 | ResNet50 | ImageNet | 28.59 | 59.56 | x | x | 10966 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_sthv2_rgb/tsn_r50_1x1x8_50e_sthv2_rgb_20210816-1aafee8f.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_sthv2_rgb/20210816_221116.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x8_50e_sthv2_rgb/20210816_221116.log.json) | | [tsn_r50_1x1x16_50e_sthv2_rgb](/configs/recognition/tsn/tsn_r50_1x1x16_50e_sthv2_rgb.py) | 高 256 | 8 | ResNet50 | ImageNet | 20.89 | 49.16 | x | x | 8337 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x16_50e_sthv2_rgb/tsn_r50_1x1x16_50e_sthv2_rgb_20210816-5d23ac6e.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x16_50e_sthv2_rgb/20210816_225256.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x16_50e_sthv2_rgb/20210816_225256.log.json) | ### Moments in Time | 配置文件 | 分辨率 | GPU 数量 | 主干网络 | 预训练 | top1 准确率 | top5 准确率 | GPU 显存占用 (M) | ckpt | log | json | | :----------------------------------------------------------------------------------- | :------: | :------: | :------: | :------: | :---------: | :---------: | :--------------: | :-----------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------: | | [tsn_r50_1x1x6_100e_mit_rgb](/configs/recognition/tsn/tsn_r50_1x1x6_100e_mit_rgb.py) | 短边 256 | 8x2 | ResNet50 | ImageNet | 26.84 | 51.6 | 8339 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x6_100e_mit_rgb/tsn_r50_1x1x6_100e_mit_rgb_20200618-d512ab1b.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x6_100e_mit_rgb/tsn_mit.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x6_100e_mit_rgb/tsn_r50_f6_mit_26.8_51.6.log.json) | ### Multi-Moments in Time | 配置文件 | 分辨率 | GPU 数量 | 主干网络 | 预训练 | mAP | GPU 显存占用 (M) | ckpt | log | json | | :------------------------------------------------------------------------------------- | :------: | :------: | :-------: | :------: | :---: | :--------------: | :-------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------: | | [tsn_r101_1x1x5_50e_mmit_rgb](/configs/recognition/tsn/tsn_r101_1x1x5_50e_mmit_rgb.py) | 短边 256 | 8x2 | ResNet101 | ImageNet | 61.09 | 10467 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r101_1x1x5_50e_mmit_rgb/tsn_r101_1x1x5_50e_mmit_rgb_20200618-642f450d.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r101_1x1x5_50e_mmit_rgb/tsn_mmit.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r101_1x1x5_50e_mmit_rgb/tsn_r101_f6_mmit_61.1.log.json) | ### ActivityNet v1.3 | 配置文件 | 分辨率 | GPU 数量 | 主干网络 | 预训练 | top1 准确率 | top5 准确率 | GPU 显存占用 (M) | ckpt | log | json | | :--------------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :------: | :---------: | :---------: | :---------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | [tsn_r50_320p_1x1x8_50e_activitynet_video_rgb](/configs/recognition/tsn/tsn_r50_320p_1x1x8_50e_activitynet_video_rgb.py) | 短边 320 | 8x1 | ResNet50 | Kinetics400 | 73.93 | 93.44 | 5692 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_50e_activitynet_video_rgb/tsn_r50_320p_1x1x8_50e_activitynet_video_rgb_20210301-7f8da0c6.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_50e_activitynet_video_rgb/20210228_223327.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_50e_activitynet_video_rgb/20210228_223327.log.json) | | [tsn_r50_320p_1x1x8_50e_activitynet_clip_rgb](/configs/recognition/tsn/tsn_r50_320p_1x1x8_50e_activitynet_clip_rgb.py) | 短边 320 | 8x1 | ResNet50 | Kinetics400 | 76.90 | 94.47 | 5692 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_50e_activitynet_clip_rgb/tsn_r50_320p_1x1x8_50e_activitynet_clip_rgb_20210301-c0f04a7e.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_50e_activitynet_clip_rgb/20210217_181313.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_50e_activitynet_clip_rgb/20210217_181313.log.json) | | [tsn_r50_320p_1x1x8_150e_activitynet_video_flow](/configs/recognition/tsn/tsn_r50_320p_1x1x8_150e_activitynet_video_flow.py) | 340x256 | 8x2 | ResNet50 | Kinetics400 | 57.51 | 83.02 | 5780 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_150e_activitynet_video_flow/tsn_r50_320p_1x1x8_150e_activitynet_video_flow_20200804-13313f52.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_150e_activitynet_video_flow/tsn_r50_320p_1x1x8_150e_activitynet_video_flow_20200804.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_150e_activitynet_video_flow/tsn_r50_320p_1x1x8_150e_activitynet_video_flow_20200804.json) | | [tsn_r50_320p_1x1x8_150e_activitynet_clip_flow](/configs/recognition/tsn/tsn_r50_320p_1x1x8_150e_activitynet_clip_flow.py) | 340x256 | 8x2 | ResNet50 | Kinetics400 | 59.51 | 82.69 | 5780 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_150e_activitynet_clip_flow/tsn_r50_320p_1x1x8_150e_activitynet_clip_flow_20200804-8622cf38.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_150e_activitynet_clip_flow/tsn_r50_320p_1x1x8_150e_activitynet_clip_flow_20200804.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_320p_1x1x8_150e_activitynet_clip_flow/tsn_r50_320p_1x1x8_150e_activitynet_clip_flow_20200804.json) | ### HVU | 配置文件\[1\] | tag 类别 | 分辨率 | GPU 数量 | 主干网络 | 预训练 | mAP | HATNet\[2\] | HATNet-multi\[2\] | ckpt | log | json | | :----------------------------------------------------------------------------------------------------------: | :-------: | :------: | :------: | :------: | :------: | :--: | :---------: | :---------------: | :--------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------: | | [tsn_r18_1x1x8_100e_hvu_action_rgb](/configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_action_rgb.py) | action | 短边 256 | 8x2 | ResNet18 | ImageNet | 57.5 | 51.8 | 53.5 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/action/tsn_r18_1x1x8_100e_hvu_action_rgb_20201027-011b282b.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/action/tsn_r18_1x1x8_100e_hvu_action_rgb_20201027.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/action/tsn_r18_1x1x8_100e_hvu_action_rgb_20201027.json) | | [tsn_r18_1x1x8_100e_hvu_scene_rgb](/configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_scene_rgb.py) | scene | 短边 256 | 8 | ResNet18 | ImageNet | 55.2 | 55.8 | 57.2 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/scene/tsn_r18_1x1x8_100e_hvu_scene_rgb_20201027-00e5748d.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/scene/tsn_r18_1x1x8_100e_hvu_scene_rgb_20201027.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/scene/tsn_r18_1x1x8_100e_hvu_scene_rgb_20201027.json) | | [tsn_r18_1x1x8_100e_hvu_object_rgb](/configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_object_rgb.py) | object | 短边 256 | 8 | ResNet18 | ImageNet | 45.7 | 34.2 | 35.1 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/object/tsn_r18_1x1x8_100e_hvu_object_rgb_20201102-24a22f30.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/object/tsn_r18_1x1x8_100e_hvu_object_rgb_20201027.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/object/tsn_r18_1x1x8_100e_hvu_object_rgb_20201027.json) | | [tsn_r18_1x1x8_100e_hvu_event_rgb](/configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_event_rgb.py) | event | 短边 256 | 8 | ResNet18 | ImageNet | 63.7 | 38.5 | 39.8 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/event/tsn_r18_1x1x8_100e_hvu_event_rgb_20201027-dea8cd71.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/event/tsn_r18_1x1x8_100e_hvu_event_rgb_20201027.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/event/tsn_r18_1x1x8_100e_hvu_event_rgb_20201027.json) | | [tsn_r18_1x1x8_100e_hvu_concept_rgb](/configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_concept_rgb.py) | concept | 短边 256 | 8 | ResNet18 | ImageNet | 47.5 | 26.1 | 27.3 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/concept/tsn_r18_1x1x8_100e_hvu_concept_rgb_20201027-fc1dd8e3.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/concept/tsn_r18_1x1x8_100e_hvu_concept_rgb_20201027.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/concept/tsn_r18_1x1x8_100e_hvu_concept_rgb_20201027.json) | | [tsn_r18_1x1x8_100e_hvu_attribute_rgb](/configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_attribute_rgb.py) | attribute | 短边 256 | 8 | ResNet18 | ImageNet | 46.1 | 33.6 | 34.9 | [ckpt](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/attribute/tsn_r18_1x1x8_100e_hvu_attribute_rgb_20201027-0b3b49d2.pth) | [log](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/attribute/tsn_r18_1x1x8_100e_hvu_attribute_rgb_20201027.log) | [json](https://download.openmmlab.com/mmaction/recognition/tsn/hvu/attribute/tsn_r18_1x1x8_100e_hvu_attribute_rgb_20201027.json) | | - | 所有 tag | 短边 256 | - | ResNet18 | ImageNet | 52.6 | 40.0 | 41.3 | - | - | - | \[1\] 简单起见，MMAction2 对每个 tag 类别训练特定的模型，作为 HVU 的基准模型。 \[2\] 这里 HATNet 和 HATNet-multi 的结果来自于 paper: [Large Scale Holistic Video Understanding](https://pages.iai.uni-bonn.de/gall_juergen/download/HVU_eccv20.pdf)。 HATNet 的时序动作候选是一个双分支的卷积网络（一个 2D 分支，一个 3D 分支），并且和 MMAction2 有相同的主干网络（ResNet18）。HATNet 的输入是 16 帧或 32 帧的长视频片段（这样的片段比 MMAction2 使用的要长），同时输入分辨率更粗糙（112px 而非 224px）。 HATNet 是在每个独立的任务（对应每个 tag 类别）上进行训练的，HATNet-multi 是在多个任务上进行训练的。由于目前没有 HATNet 的开源代码和模型，这里仅汇报了原 paper 的精度。 注： 1. 这里的 **GPU 数量** 指的是得到模型权重文件对应的 GPU 个数。默认地，MMAction2 所提供的配置文件对应使用 8 块 GPU 进行训练的情况。 依据 [线性缩放规则](https://arxiv.org/abs/1706.02677)，当用户使用不同数量的 GPU 或者每块 GPU 处理不同视频个数时，需要根据批大小等比例地调节学习率。 如，lr=0.01 对应 4 GPUs x 2 video/gpu，以及 lr=0.08 对应 16 GPUs x 4 video/gpu。 2. 这里的 **推理时间** 是根据 [基准测试脚本](/tools/analysis/benchmark.py) 获得的，采用测试时的采帧策略，且只考虑模型的推理时间， 并不包括 IO 时间以及预处理时间。对于每个配置，MMAction2 使用 1 块 GPU 并设置批大小（每块 GPU 处理的视频个数）为 1 来计算推理时间。 3. 参考代码的结果是通过使用相同的模型配置在原来的代码库上训练得到的。 4. 我们使用的 Kinetics400 验证集包含 19796 个视频，用户可以从 [验证集视频](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155136485_link_cuhk_edu_hk/EbXw2WX94J1Hunyt3MWNDJUBz-nHvQYhO9pvKqm6g39PMA?e=a9QldB) 下载这些视频。同时也提供了对应的 [数据列表](https://download.openmmlab.com/mmaction/dataset/k400_val/kinetics_val_list.txt) （每行格式为：视频 ID，视频帧数目，类别序号）以及 [标签映射](https://download.openmmlab.com/mmaction/dataset/k400_val/kinetics_class2ind.txt) （类别序号到类别名称）。 对于数据集准备的细节，用户可参考： - [准备 ucf101](/tools/data/ucf101/README_zh-CN.md) - [准备 kinetics](/tools/data/kinetics/README_zh-CN.md) - [准备 sthv1](/tools/data/sthv1/README_zh-CN.md) - [准备 sthv2](/tools/data/sthv2/README_zh-CN.md) - [准备 mit](/tools/data/mit/README_zh-CN.md) - [准备 mmit](/tools/data/mmit/README_zh-CN.md) - [准备 hvu](/tools/data/hvu/README_zh-CN.md) - [准备 hmdb51](/tools/data/hmdb51/README_zh-CN.md) ## 如何训练 用户可以使用以下指令进行模型训练。 ```shell python tools/train.py ${CONFIG_FILE} [optional arguments] ``` 例如：以一个确定性的训练方式，辅以定期的验证过程进行 TSN 模型在 Kinetics-400 数据集上的训练。 ```shell python tools/train.py configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py \ --work-dir work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb \ --validate --seed 0 --deterministic ``` 更多训练细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#训练配置) 中的 **训练配置** 部分。 ## 如何测试 用户可以使用以下指令进行模型测试。 ```shell python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments] ``` 例如：在 Kinetics-400 数据集上测试 TSN 模型，并将结果导出为一个 json 文件。 ```shell python tools/test.py configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py \ checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \ --out result.json ``` 更多测试细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#测试某个数据集) 中的 **测试某个数据集** 部分。