Comments (4)
Different hardwares lead to different accuracies. Tuning the learning rate and using gradient accumulation may achieve the performance.
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Thanks for your comment!
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I ran your implemented code using J-AAGCN and NTU-RGBD CV dataset.
But Accuracy is 94.64, not 95.1 in your paper.
What is the difference?
The batch size was 32, not 64 because of the resource limit.
Are there any other things to be aware of? I use your implemented code.
Have you successfully ran out of 95.1? How to set your learning rate, can you ask?
from 2s-agcn.
I ran your implemented code using J-AAGCN and NTU-RGBD CV dataset.
But Accuracy is 94.64, not 95.1 in your paper.
What is the difference?
The batch size was 32, not 64 because of the resource limit.
Are there any other things to be aware of? I use your implemented code.
Can AAGCN's attention explain why convolution can be used and the residual operation code is not reflected. Can you talk about the process? Why does the paper here say that it is a feature map, an attention map, and a graph convolution operation.
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Related Issues (20)
- 关于MS-AAGCN的运行问题 HOT 2
- 请问模型的总参数量和FLOPS是多少呀
- list of packages to install ? HOT 1
- pretrained weights
- 请问一下最新的MSAAGCN的融合4个流的代码有没有啊
- question about preprocess
- how to visualize the Adjacency matrix? HOT 4
- 2sagcn请问是否支持多人行为识别
- 请问有人知道怎么可视化论文中的图8和9吗 就是人体骨架图的可视化
- Where is spatial configuration partitioning? HOT 2
- 请问self.mask是在哪里定义的? HOT 1
- How can I get the first layer weight of AGCN?
- 训练完成后,执行 testXX.yaml 出错。
- 数据预测处理报错,invalid literal for int() with base 10: 'sam'
- the question about max sample.
- How to generate a graph of adjacency matrix HOT 1
- Where is the code for visualization in Figure 8 and 9? HOT 3
- 关于自适应时,tanh和softmax函数的问题
- 注意力模块中参数A的定义 HOT 1
- 关于注意力模块中调用的参数A的问题
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