PaddleVideo v2.1.0

Release Note

PaddleVideo v2.1.0有如下升级点：

框架

1. 重构framework架构，单卡和多卡下forward接口统一。
2. 重构Inference架构，支持不同模型预测。
3. 添加混合精度训练和分布式训练接口。

模型

1. PP-TSM
   (1) 通过添加tricks，Uniform评估策略下精度由73.5提升至74.54。
   (2) 添加dense训练策略，蒸馏精度达到76.16，同等ResNet50 backbone下精度超过slowfast。
2. Slowfast
   (1) 添加multigrid训练加速策略，在kinetics-400数据集上训练358个epoch仅需6.7天。
   (2) 评估精度由74.35提升至75.84。
3. BMN
   (1) 添加Inference支持。

数据集

1. 提供Kinetics-400数据集下载链接，包括百度网盘下载和脚本下载方式。

应用

1. FootballAction:
   (1) 基础特征模型由TSN替换为ppTSM，准确率由84%提升到94%。
   (2) 准确率提升，precision和recall均有大幅提升，F1-score从0.57提升到0.82。

Release Note

Framework

1. Refactoring code of model.framework to unify the forward interface of single card and multi card training.
2. Refactoring code of utils.inference to support different model predictions.
3. Add interface of Automatic Mixed Precision Training and Distributed training.

Model

1. PP-TSM
   (1) Improve accuracy from 73.5 to 74.54 using uniform sampling method.
   (2) Improve accuracy to 76.16 using dense sampling method.
2. Slowfast
   (1) Add multigrid training strategy. It only takes 6.7 days to train 358 epochs on the kinetics-400 dataset using v100.
   (2) Improve accuracy from 74.35 to 75.84.
3. BMN
   (1) Support inference.

Dataset

1. Provide the download link of kinetics-400 dataset, including Baidu network disk and script download.

Application

1. FootballAction
   (1) Replace TSN with PP-TSM, and the accuracy is improved from 84% to 94%.
   (2) improve F1 score from 0.57 to 0.82.