# CSN ## 简介 ```BibTeX @inproceedings{inproceedings, author = {Wang, Heng and Feiszli, Matt and Torresani, Lorenzo}, year = {2019}, month = {10}, pages = {5551-5560}, title = {Video Classification With Channel-Separated Convolutional Networks}, doi = {10.1109/ICCV.2019.00565} } ``` ```BibTeX @inproceedings{ghadiyaram2019large, title={Large-scale weakly-supervised pre-training for video action recognition}, author={Ghadiyaram, Deepti and Tran, Du and Mahajan, Dhruv}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={12046--12055}, year={2019} } ``` ## 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 | | :------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :-------: | :------: | :---------: | :---------: | :----------------: | :--------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------: | | [ircsn_bnfrozen_r50_32x2x1_180e_kinetics400_rgb](/configs/recognition/csn/ircsn_bnfrozen_r50_32x2x1_180e_kinetics400_rgb.py) | 短边 320 | x | ResNet50 | None | 73.6 | 91.3 | x | x | [ckpt](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_bnfrozen_r50_32x2x1_180e_kinetics400_rgb/ircsn_bnfrozen_r50_32x2x1_180e_kinetics400_rgb_20210618-4e29e2e8.pth) | [log](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_bnfrozen_r50_32x2x1_180e_kinetics400_rgb/20210618_182414.log) | [json](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_bnfrozen_r50_32x2x1_180e_kinetics400_rgb/20210618_182414.log.json) | | [ircsn_ig65m_pretrained_bnfrozen_r50_32x2x1_58e_kinetics400_rgb](/configs/recognition/csn/ircsn_ig65m_pretrained_bnfrozen_r50_32x2x1_58e_kinetics400_rgb.py) | 短边 320 | x | ResNet50 | IG65M | 79.0 | 94.2 | x | x | [infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ircsn_ig65m_pretrained_r50_32x2x1_58e_kinetics400_rgb_20210617-86d33018.pth) | x | x | | [ircsn_bnfrozen_r152_32x2x1_180e_kinetics400_rgb](/configs/recognition/csn/ircsn_bnfrozen_r152_32x2x1_180e_kinetics400_rgb.py) | 短边 320 | x | ResNet152 | None | 76.5 | 92.1 | x | x | [infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ircsn_from_scratch_r152_32x2x1_180e_kinetics400_rgb_20210617-5c933ae1.pth) | x | x | | [ircsn_sports1m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb](/configs/recognition/csn/ircsn_sports1m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb.py) | 短边 320 | x | ResNet152 | Sports1M | 78.2 | 93.0 | x | x | [infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ircsn_sports1m_pretrained_r152_32x2x1_58e_kinetics400_rgb_20210617-b9b10241.pth) | x | x | | [ircsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb.py](/configs/recognition/csn/ircsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb.py) | 短边 320 | 8x4 | ResNet152 | IG65M | 82.76/82.6 | 95.68/95.3 | x | 8516 | [ckpt](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb/ircsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb_20200812-9037a758.pth)/[infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb_20210617-e63ee1bd.pth) | [log](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb/20200809_053132.log) | [json](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb/20200809_053132.log.json) | | [ipcsn_bnfrozen_r152_32x2x1_180e_kinetics400_rgb](/configs/recognition/csn/ipcsn_bnfrozen_r152_32x2x1_180e_kinetics400_rgb.py) | 短边 320 | x | ResNet152 | None | 77.8 | 92.8 | x | x | [infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ipcsn_from_scratch_r152_32x2x1_180e_kinetics400_rgb_20210617-d565828d.pth) | x | x | | [ipcsn_sports1m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb](/configs/recognition/csn/ipcsn_sports1m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb.py) | 短边 320 | x | ResNet152 | Sports1M | 78.8 | 93.5 | x | x | [infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ipcsn_sports1m_pretrained_r152_32x2x1_58e_kinetics400_rgb_20210617-3367437a.pth) | x | x | | [ipcsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb](/configs/recognition/csn/ipcsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb.py) | 短边 320 | x | ResNet152 | IG65M | 82.5 | 95.3 | x | x | [infer_ckpt](https://download.openmmlab.com/mmaction/recognition/csn/vmz/vmz_ipcsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb_20210617-c3be9793.pth) | x | x | | [ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb.py](/configs/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb.py) | 短边 320 | 8x4 | ResNet152 | IG65M | 80.14 | 94.93 | x | 8517 | [ckpt](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb_20200803-fc66ce8d.pth) | [log](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb/20200728_031952.log) | [json](https://download.openmmlab.com/mmaction/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb/20200728_031952.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. 这里使用的 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）。 4. 这里的 **infer_ckpt** 表示该模型权重文件是从 [VMZ](https://github.com/facebookresearch/VMZ) 导入的。 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 CSN 模型在 Kinetics400 数据集上的训练。 ```shell python tools/train.py configs/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb.py \ --work-dir work_dirs/ircsn_ig65m_pretrained_r152_32x2x1_58e_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#%E8%AE%AD%E7%BB%83%E9%85%8D%E7%BD%AE) 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 CSN 模型，并将结果导出为一个 json 文件。 ```shell python tools/test.py configs/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_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#%E6%B5%8B%E8%AF%95%E6%9F%90%E4%B8%AA%E6%95%B0%E6%8D%AE%E9%9B%86) 中的 **测试某个数据集** 部分。