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There is a set of Pixel Level transforms that is used in the work Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

The authors also share the code => we can absorb some transforms that they have into the library.

https://github.com/hendrycks/robustness/blob/master/ImageNet-C/create_c/make_imagenet_c.py

• ShotNoise
• Defocus
• GlassBlur

There are many links in Kinetics that have expired. As as result, everyone might not be using the same Kinetics dataset. As a reference, the statistics of the Kinetics dataset used in PySlowFast can be found here, https://github.com/facebookresearch/video-nonlocal-net/blob/master/DATASET.md. However, I cannot seem to find similar information for gluoncv. Will you guys be sharing the statistics and

how to use gui in the AidLearning?how to custom the gui?

• 1. 准备数据，制作训练时需要的文件（data文件夹下面的四个文件：数据类名，锚框，训练数据，测试数据）
• 2. 根据步骤1修改core/config.py文件
• 3. 基于yolo原始权重训练，（或者从头训练）（这步教程写的很清楚）
• 4. 当loss为1左右时，模型就已经有效果了，训练过程中会保存很多模型，选择一个loss最小的，并根据所选择的模型名修改freeze_graph.py，修改后运行freeze_graph.py，会在项目根目录生成***.pb文件
• 5. 修改demo文件，如修改video_demo.py，修改调用的***.pb文件路径，修改num_class为自己数据类个数