Comments (6)
972238672
commented on July 27, 2024
我使用了作者提供的预训练权重训练后最好的效果中MAP50达到了0.822
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jacksonwu09
commented on July 27, 2024
我使用了作者提供的预训练权重训练后最好的效果中MAP50达到了0.822
您好，我想问下，你就直接使用作者提供的代码，没有修改，使用作者提供的fold1权重运行后就0,822吗，你使用从头训练的权重的map50有多少
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972238672
commented on July 27, 2024
很抱歉我没有从头训练过
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jacksonwu09
commented on July 27, 2024
你好，我想问下 这个mAP是仅在fold11上的吗，那如果比较的话，是与下图中哪个图进行比较
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farhaniqbalbajwa
commented on July 27, 2024
I think the results you provided are only of fold 1. but in paper he averaged the mAP of all 10 folds which is 75.09.
mAP of fold1 is 80.9
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jacksonwu09
commented on July 27, 2024
I think the results you provided are only of fold 1. but in paper he averaged the mAP of all 10 folds which is 75.09. mAP of fold1 is 80.9
Thank you for your answer, I have figured it out.
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Related Issues (20)
- 请问MF模块的输入为什么是256尺寸的，imgsz不是1024吗？
- 关于MF过程的疑问 HOT 1
- AttributeError: 'Model' object has no attribute 'steam'
- AttributeError: module 'numpy' has no attribute 'int'. HOT 4
- Question about annotation HOT 2
- 请问loss.py中补充的LevelAttention_loss有使用到吗
- 测试使用作者提供的预训练权重问题 HOT 1
- 请问作者在export过程中是否有遇到过索引越界问题，具体问题在commom.py的MF类中 HOT 1
- 测试模型参数和GFLOPs问题 HOT 5
- 作者什么时候能更新superyolo版本的export.py HOT 1
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- 训练时无法找到标签
- Welcome to SuperYOLO Discussions! HOT 6
- SR的效果如何体现 HOT 3
- About multimodal results on YOLOv5
- 请问VEDAI数据集为什么在论文结果对比和代码中只保留了8类，删去了vehicle呢？
- SR网络 HOT 1
- Tag corresponding category
- SR分支
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