在图像分类任务中使用口罩
我正在处理图像分类问题，数据集采用以下格式：
- class_1_folder：由images_folder和mask_folder and mask_folder
- ：由images_folder和mask_folder组成
class_2_folder 通常，分类任务。
我做了一些研究，但找不到可以理解的答案。
我的想法是，我可以通过帮助专注于图像的特定部分来利用它们来改善分类器的度量结果。这有效吗？如果是，我应该在将图像馈送给分类器之前考虑图像的卷积吗？
任何帮助都非常感谢。谢谢。
I am working on an image classification problem and the dataset comes in the following format :
- class_1_folder : consists of images_folder and mask_folder
- class_2_folder : consists of images_folder and mask_folder
I am quite new to the field and would like your advice on the use of the mask folders in general in classification tasks.
I did some research but I cannot find an understandable answer.
My thought is that I could use them to improve my classifier's metric results by helping to focus on specific parts of the image. Is this valid ? If yes, should I consider the convolution of the image with its mask before feeding the image to a classifier ?
Any help is much appreciated. Thank you.
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