Comments (5)
Linzaer
commented on May 22, 2024
多大的输入？多核吗？是只有cnn推理时间还是包括前置的图片处理和后处理？可以尝试ncnn 的int8量化、换slim版本的模型、根据落地场景选择降低输入分辨率(比如160x120、128x96)。
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luliang6
commented on May 22, 2024
@Linzaer 输入为320×240，默认4核，只计算了cnn推理时间，无前后处理，落地场景是手机前置摄像头以640×480的分辨率输入做人脸识别。
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Linzaer
commented on May 22, 2024
640x480输入算比较大了，推理计算量相当于320x240的4倍左右，这个时间应该差不多。如果你的场景距离要求在6m以内的话，320x240就应该够了。输入大小很影响推理速度的。
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luliang6
commented on May 22, 2024
谢谢解答，我改小分辨率试试看。
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Linzaer
commented on May 22, 2024
好的，不客气，也可以试下slim版本，会快一点。
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Related Issues (20)
- A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, None, 2), (None, 4420, 4)]
- How to increase amount of using CPUs?
- 请问可以批量推理吗
- About the BBox Detection for "masked-Face"
- ModuleNotFoundError: No module named 'vision'
- error building for ncnn
- 请问README中的测试精度是指什么？
- Error while training HOT 1
- Bounding box overlap issue.
- Min_boxes (anchors) calculation
- 请问如何训练灰度图？ HOT 1
- ModuleNotFoundError: No module named 'tf' in convert_tensorflow.py HOT 1
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- 对全景图进行人脸识别
- 有一段代码不是很理解，有哪位大佬帮我解下惑 HOT 1
- 代码参数理解 HOT 1
- Improve accuracy of the ultraface-rfb-640.onnx model
- 如何优化GPU训练速度
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