Comments (5)
lijiannuist
commented on April 28, 2024
Hi @gtgtgt1117
对于100长特征而言，proposals的数量是固定的，有100*100/2，PFG layer其实是生成这些个proposals的特征，这些个proposals肯定是有重叠的，靠后面的两个分支进行打分和后处理去重。
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gtgtgt1117
commented on April 28, 2024
Hi @gtgtgt1117
对于100长特征而言，proposals的数量是固定的，有100*100/2，PFG layer其实是生成这些个proposals的特征，这些个proposals肯定是有重叠的，靠后面的两个分支进行打分和后处理去重。
Thank you .但是我并不是很理解100*100/2.
那这个可以用于实际场景的检测吗?我的测试结果并不是很好(指的是时间段和实际不是很符合)
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gtgtgt1117
commented on April 28, 2024
@lijiannuist Hi,proposals的个数应该是1-100的和吧(5050个)? 在后处理中通过top_number来设置最后保留的proposals个数是不?
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lijiannuist
commented on April 28, 2024
@gtgtgt1117
是的，一般还需要卡一个分数阈值来保留。
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gtgtgt1117
commented on April 28, 2024
@gtgtgt1117
是的，一般还需要卡一个分数阈值来保留。
Thank you
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Related Issues (20)
- The original ActivityNet TSN feature HOT 1
- 能否放一下thumos上的实验？
- features csv different from provided
- HOW ABOUT THE SPEED? HOT 1
- THUMOS14
- Question about PFG layer HOT 2
- About the provided features！！ HOT 1
- run error HOT 1
- proposal HOT 1
- Segmentation fault when define the whole model with PFG layer compiled HOT 2
- why the "annotations" of testing is none?
- Compile tensorflow-version proposal feature generation layers HOT 1
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