Describe your issue
skimage.transform.resize returns float images in [0.0, 1.0]. The ResNet50 preprocess_input expects image pixel values in the 0–255 range. If the image is not scaled up, the preprocessing is incorrect and the model produces wrong predictions.
Steps to reproduce
Run the notebook cell that resizes the image and calls preprocess_input (in section Classification of an Image in lab_4.ipynb).
Observe decoded predictions; they will be incorrect or meaningless.
What was the expected result?
preprocess_input should receive image values in the 0–255 range (float32 or uint8) so that the model preprocessing (mean subtraction / scaling) works as intended and predictions are meaningful.
Put here any screenshots or videos (optional)
No response
Put here the code owner you'd like to review this issue.
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Describe your issue
skimage.transform.resize returns float images in [0.0, 1.0]. The ResNet50 preprocess_input expects image pixel values in the 0–255 range. If the image is not scaled up, the preprocessing is incorrect and the model produces wrong predictions.
Steps to reproduce
Run the notebook cell that resizes the image and calls preprocess_input (in section Classification of an Image in lab_4.ipynb).
Observe decoded predictions; they will be incorrect or meaningless.
What was the expected result?
preprocess_input should receive image values in the 0–255 range (float32 or uint8) so that the model preprocessing (mean subtraction / scaling) works as intended and predictions are meaningful.
Put here any screenshots or videos (optional)
No response
Put here the code owner you'd like to review this issue.
No response