Comments (11)
1.按照我训练的结果和centerface和retinaface比较过,我的都更准
2.relu6可以替换成relu,结果差距不大
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谢谢回复,我这边只是用centernet训练了几个版本不带关键点 检测率也比原始centerface
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关键点加进去就相当于multipose,会对检出率有帮助
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只是说这个时候的关键点不再是人体的而是人脸的 看了下改动也不大,。对于单纯检测我直接voc格式的 因为如果有新任务可以标注直接训练的
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谢谢回复,我这边只是用centernet训练了几个版本不带关键点 检测率也比原始centerface
你这边训练的结果超过了官方的指标么?指标咋样啊?我们这边的指标才76%,还在调整的过程中,能加你个联系方式么?我的邮箱[email protected],能不能发一个联系方式呢?直接在上面弄手机号不太好吧
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没有测试具体指标,因为widerface里面有一些人脸质量不适合人脸识别,所以过滤掉了,很难做统一的评价
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没有测试具体指标,因为widerface里面有一些人脸质量不适合人脸识别,所以过滤掉了,很难做统一的评价
相当的给力啊,像你这样的大牛学习啊,只能怨自己太菜了,你的工作很有意义,优秀皆开源,在下佩服
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CenterMulti: 人脸检测+landmark可参考CenterMulti,效果更好,而且支持landmark的confidence
1.怎么给landmark增加confidence的训练?
2.在centermulti里面可以参考哪一个backbone?能输出confidence的是哪一个?
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1.landmark可以理解为姿态估计的每个关键点,参见multi_pose.py def show_results(self, debugger, image, results)
2.所有backbone都可以输出conference
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请问landmark能像openpose那样输出每个点的置信度么
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sure
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Related Issues (20)
- Center Face Demo? HOT 2
- Can you provide a mobilenet-v2 config file. HOT 1
- small网络结果不对? HOT 3
- 换backbone后如何训练? HOT 1
- Questions about edges and acc_idxs HOT 3
- dataset for head-shoulder HOT 1
- where are these files? HOT 1
- 如何利用自己的数据进行再训练呢 HOT 1
- 请问你用什么方式对质量不好的人脸进行的过滤呢?
- 请问keypoint or pose中模型的测试脚本用哪个呢 HOT 1
- the head? HOT 2
- 可以分享一下 COCO_HP里具体都做了哪些更改吗
- 如何获取TensorRT文件夹下程序依赖的代码 HOT 1
- 可以提供下人脸mobilenetv2模型对应的mobilenetv2_512x512.yaml文件吗? HOT 2
- 请问centerface做了哪些修改呢 HOT 15
- shoulder.png使用的模型在CenterNet MODEL_ZOO吗? HOT 1
- 头肩模型训练的配置文件能分享一下吗 HOT 1
- 训练相关问题 HOT 3
- 骨干网络应用问题 HOT 2
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