TrainImageContentDetector

TrainImageContentDetector[{img1{bbox1class1,},}]

根据所给的样例训练 ContentDetectorFunction[].

更多信息和选项

范例

打开所有单元关闭所有单元

基本范例  (1)

训练一个简单的物体检测器:

对新的图像应用该检测器:

突出显示在输入图像上检测出的结果:

选项  (5)

PerformanceGoal  (1)

PerformanceGoal"Quality" 强调结果的质量:

PerformanceGoal"Speed" 强调计算的速度:

ProgressReporting  (1)

默认情况下,在动态面板中报告进度:

ProgressReportingFalse 禁止显示进度面板:

TargetDevice  (1)

可用的情况下,使用默认的系统 GPU 来训练检测器:

如果没有兼容的 GPU,则发出一条消息:

TimeGoal  (1)

训练时间可能受多种因素影响,如样例和类别的数量:

TimeGoal 指定额定的训练时间:

ValidationSet  (1)

默认情况下,只在检测器上执行交叉验证:

ValidationSet 提供单独的验证样例:

Wolfram Research (2021),TrainImageContentDetector,Wolfram 语言函数,https://reference.wolfram.com/language/ref/TrainImageContentDetector.html.

文本

Wolfram Research (2021),TrainImageContentDetector,Wolfram 语言函数,https://reference.wolfram.com/language/ref/TrainImageContentDetector.html.

CMS

Wolfram 语言. 2021. "TrainImageContentDetector." Wolfram 语言与系统参考资料中心. Wolfram Research. https://reference.wolfram.com/language/ref/TrainImageContentDetector.html.

APA

Wolfram 语言. (2021). TrainImageContentDetector. Wolfram 语言与系统参考资料中心. 追溯自 https://reference.wolfram.com/language/ref/TrainImageContentDetector.html 年

BibTeX

@misc{reference.wolfram_2024_trainimagecontentdetector, author="Wolfram Research", title="{TrainImageContentDetector}", year="2021", howpublished="\url{https://reference.wolfram.com/language/ref/TrainImageContentDetector.html}", note=[Accessed: 18-November-2024 ]}

BibLaTeX

@online{reference.wolfram_2024_trainimagecontentdetector, organization={Wolfram Research}, title={TrainImageContentDetector}, year={2021}, url={https://reference.wolfram.com/language/ref/TrainImageContentDetector.html}, note=[Accessed: 18-November-2024 ]}