Open ONobody opened 1 year ago
For creating your own dataset, simply dump all of your images into a directory with ".jpg", ".jpeg", or ".png" extensions. If you wish to train a class-conditional model, name the files like "mylabel1_XXX.jpg", "mylabel2_YYY.jpg", etc., so that the data loader knows that "mylabel1" and "mylabel2" are the labels. Subdirectories will automatically be enumerated as well, so the images can be organized into a recursive structure (although the directory names will be ignored, and the underscore prefixes are used as names).
The images will automatically be scaled and center-cropped by the data-loading pipeline. Simply pass --data_dir path/to/images to the training script, and it will take care of the rest.
like this Are the pictures in these folders named in the form of mylabel?
dog_01.jpg, dog_02.jpg......cat_01.jpg, cat_02.jpg... in one folder
When the categories of my data are eight Do you need to make any changes to the code? thank you
No,you only need to change the name of file
Okay, I'll try. Thank you.
Dear author, I used my own dataset as you said, but image_datasets.py appears "TypeError: init() got multiple values for argument 'classes' "error, is the source code or my data problem
dog_01.jpg,dog_02.jpg...cat_01.jpg,cat_02.jpg...在一个文件夹中
通过这种方式训练出一个分类器,我该怎么让它引导生成不同类别的图像呢
通过这种方式训练出一个分类器,我该怎么让它引导生成不同类别的图像呢
好的,我试试。谢谢。
你好,我想知道通过这种方式训练出一个分类器,我该怎么让它引导生成不同类别的图像呢
tomatically be scaled and center-cropped by the data-loading pipeline. Simply pass --data_dir path/to/images to the training script, and it will take care of the rest.
用classifier_sample.py 把输入的类别控制一下就行
*用classifier_sample.py 把输入的类别控制一下就行 你好 具体怎么控制呢
tomatically be scaled and center-cropped by the data-loading pipeline. Simply pass --data_dir path/to/images to the training script, and it will take care of the rest.
用classifier_sample.py 把输入的类别控制一下就行
你好 这里具体怎么控制呢
Can anyone help? Thank you very much.