# I3D ## 简介 ```BibTeX @inproceedings{inproceedings, author = {Carreira, J. and Zisserman, Andrew}, year = {2017}, month = {07}, pages = {4724-4733}, title = {Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset}, doi = {10.1109/CVPR.2017.502} } ``` ```BibTeX @article{NonLocal2018, author = {Xiaolong Wang and Ross Girshick and Abhinav Gupta and Kaiming He}, title = {Non-local Neural Networks}, journal = {CVPR}, year = {2018} } ``` ## 模型库 ### 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 | 推理时间 (video/s) | GPU 显存占用 (M) | ckpt | log | json | | :----------------------------------------------------------------------------------------------------------------------------------------------- | :-------: | :------: | :------: | :------: | :---------: | :---------: | :----------------: | :--------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------: | | [i3d_r50_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb.py) | 340x256 | 8 | ResNet50 | ImageNet | 72.68 | 90.78 | 1.7 (320x3 frames) | 5170 | [ckpt](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb/i3d_r50_32x2x1_100e_kinetics400_rgb_20200614-c25ef9a4.pth) | [log](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb/20200614_060456.log) | [json](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb/20200614_060456.log.json) | | [i3d_r50_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb.py) | 短边 256 | 8 | ResNet50 | ImageNet | 73.27 | 90.92 | x | 5170 | [ckpt](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_256p_32x2x1_100e_kinetics400_rgb/i3d_r50_256p_32x2x1_100e_kinetics400_rgb_20200801-7d9f44de.pth) | [log](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_256p_32x2x1_100e_kinetics400_rgb/20200725_031555.log) | [json](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_256p_32x2x1_100e_kinetics400_rgb/20200725_031555.log.json) | | [i3d_r50_video_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_r50_video_32x2x1_100e_kinetics400_rgb.py) | 短边 256p | 8 | ResNet50 | ImageNet | 72.85 | 90.75 | x | 5170 | [ckpt](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_video_32x2x1_100e_kinetics400_rgb/i3d_r50_video_32x2x1_100e_kinetics400_rgb_20200826-e31c6f52.pth) | [log](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_video_32x2x1_100e_kinetics400_rgb/20200706_143014.log) | [json](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_video_32x2x1_100e_kinetics400_rgb/20200706_143014.log.json) | | [i3d_r50_dense_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_r50_dense_32x2x1_100e_kinetics400_rgb.py) | 340x256 | 8x2 | ResNet50 | ImageNet | 72.77 | 90.57 | 1.7 (320x3 frames) | 5170 | [ckpt](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_dense_32x2x1_100e_kinetics400_rgb/i3d_r50_dense_32x2x1_100e_kinetics400_rgb_20200616-2bbb4361.pth) | [log](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_dense_32x2x1_100e_kinetics400_rgb/20200616_230011.log) | [json](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_dense_32x2x1_100e_kinetics400_rgb/20200616_230011.log.json) | | [i3d_r50_dense_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_r50_dense_32x2x1_100e_kinetics400_rgb.py) | 短边 256 | 8 | ResNet50 | ImageNet | 73.48 | 91.00 | x | 5170 | [ckpt](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_dense_256p_32x2x1_100e_kinetics400_rgb/i3d_r50_dense_256p_32x2x1_100e_kinetics400_rgb_20200725-24eb54cc.pth) | [log](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_dense_256p_32x2x1_100e_kinetics400_rgb/20200725_031604.log) | [json](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_dense_256p_32x2x1_100e_kinetics400_rgb/20200725_031604.log.json) | | [i3d_r50_lazy_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_r50_lazy_32x2x1_100e_kinetics400_rgb.py) | 340x256 | 8 | ResNet50 | ImageNet | 72.32 | 90.72 | 1.8 (320x3 frames) | 5170 | [ckpt](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_fast_32x2x1_100e_kinetics400_rgb/i3d_r50_fast_32x2x1_100e_kinetics400_rgb_20200612-000e4d2a.pth) | [log](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_fast_32x2x1_100e_kinetics400_rgb/20200612_233836.log) | [json](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_fast_32x2x1_100e_kinetics400_rgb/20200612_233836.log.json) | | [i3d_r50_lazy_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_r50_lazy_32x2x1_100e_kinetics400_rgb.py) | 短边 256 | 8 | ResNet50 | ImageNet | 73.24 | 90.99 | x | 5170 | [ckpt](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_fast_256p_32x2x1_100e_kinetics400_rgb/i3d_r50_fast_256p_32x2x1_100e_kinetics400_rgb_20200817-4e90d1d5.pth) | [log](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_fast_256p_32x2x1_100e_kinetics400_rgb/20200725_031457.log) | [json](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_r50_fast_256p_32x2x1_100e_kinetics400_rgb/20200725_031457.log.json) | | [i3d_nl_embedded_gaussian_r50_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_nl_embedded_gaussian_r50_32x2x1_100e_kinetics400_rgb.py) | 短边 256p | 8x4 | ResNet50 | ImageNet | 74.71 | 91.81 | x | 6438 | [ckpt](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_nl_embedded_gaussian_r50_32x2x1_100e_kinetics400_rgb/i3d_nl_embedded_gaussian_r50_32x2x1_100e_kinetics400_rgb_20200813-6e6aef1b.pth) | [log](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_nl_embedded_gaussian_r50_32x2x1_100e_kinetics400_rgb/20200813_034054.log) | [json](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_nl_embedded_gaussian_r50_32x2x1_100e_kinetics400_rgb/20200813_034054.log.json) | | [i3d_nl_gaussian_r50_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_nl_gaussian_r50_32x2x1_100e_kinetics400_rgb.py) | 短边 256p | 8x4 | ResNet50 | ImageNet | 73.37 | 91.26 | x | 4944 | [ckpt](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_nl_gaussian_r50_32x2x1_100e_kinetics400_rgb/i3d_nl_gaussian_r50_32x2x1_100e_kinetics400_rgb_20200815-17f84aa2.pth) | [log](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_nl_gaussian_r50_32x2x1_100e_kinetics400_rgb/20200813_034909.log) | [json](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_nl_gaussian_r50_32x2x1_100e_kinetics400_rgb/20200813_034909.log.json) | | [i3d_nl_dot_product_r50_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_nl_dot_product_r50_32x2x1_100e_kinetics400_rgb.py) | 短边 256p | 8x4 | ResNet50 | ImageNet | 73.92 | 91.59 | x | 4832 | [ckpt](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_nl_dot_product_r50_32x2x1_100e_kinetics400_rgb/i3d_nl_dot_product_r50_32x2x1_100e_kinetics400_rgb_20200814-7c30d5bb.pth) | [log](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_nl_dot_product_r50_32x2x1_100e_kinetics400_rgb/20200814_044208.log) | [json](https://download.openmmlab.com/mmaction/recognition/i3d/i3d_nl_dot_product_r50_32x2x1_100e_kinetics400_rgb/20200814_044208.log.json) | 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. 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 [数据集准备文档](/docs/zh_cn/data_preparation.md) 中的 Kinetics400 部分。 ## 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 I3D 模型在 Kinetics400 数据集上的训练。 ```shell python tools/train.py configs/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb.py \ --work-dir work_dirs/i3d_r50_32x2x1_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 Kinetics400 数据集上测试 I3D 模型，并将结果导出为一个 json 文件。 ```shell python tools/test.py configs/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb.py \ checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \ --out result.json --average-clips prob ``` 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。