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python - 使用包含图像的 3 个文件夹创建用于训练和测试的数据

问题描述

我在跑步：

path = Path('/content/drive/MyDrive/X-Ray_Image_DataSet')
np.random.seed(41)
data = ImageDataBunch.from_folder(dta, train="Train", valid ="Valid", ds_tfms=get_transforms(),size=(256,256), bs=32, num_workers=4).normalize()

我收到了这个错误：

/usr/local/lib/python3.7/dist-packages/fastai/data_block.py:458: UserWarning: Your training set is empty. If this is by design, pass `ignore_empty=True` to remove this warning.
  warn("Your training set is empty. If this is by design, pass `ignore_empty=True` to remove this warning.")
/usr/local/lib/python3.7/dist-packages/fastai/data_block.py:461: UserWarning: Your validation set is empty. If this is by design, use `split_none()`
                 or pass `ignore_empty=True` when labelling to remove this warning.
  or pass `ignore_empty=True` when labelling to remove this warning.""")
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/fastai/data_block.py in get_label_cls(self, labels, label_cls, label_delim, **kwargs)
    264         if label_delim is not None:             return MultiCategoryList
--> 265         try: it = index_row(labels,0)
    266         except: raise Exception("""Can't infer the type of your targets. 

7 frames
IndexError: index 0 is out of bounds for axis 0 with size 0

During handling of the above exception, another exception occurred:

Exception                                 Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/fastai/data_block.py in get_label_cls(self, labels, label_cls, label_delim, **kwargs)
    265         try: it = index_row(labels,0)
    266         except: raise Exception("""Can't infer the type of your targets. 
--> 267 It's either because your data source is empty or because your labelling function raised an error.""")
    268         if isinstance(it, (float, np.float32)): return FloatList
    269         if isinstance(try_int(it), (str, Integral)):  return CategoryList

Exception: Can't infer the type of your targets. 
It's either because your data source is empty or because your labelling function raised an error.

标签： pythonimage-classificationtrain-test-split

解决方案

np.random.seed(41)
data = ImageDataBunch.from_folder(path, train = '.', valid_pct=0.2,
        ds_tfms=get_transforms(), size=(256,256), bs=32, num_workers=4).normalize()

你可以用这个代替那个

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