how can i make alexnet accept 277x277x1 images
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i got error in example TransferLearningUsingAlexNetExample Error using trainNetwork (line 140) The training images are of size 227x227x1 but the input layer expects images of size 227x227x3. my dataset 277X277x1
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arun anoop m
2020-7-17
a=imread(' ');
l=alexnet;
a=imresize(a,[227 227]);
s=classify(l,a);
imshow(a);
try this.
Eline Geerits
2022-4-4
How can I apply this to an DataSet. I have abaout 4000 Images I have to resize so it would take way to long to do this for every Image indevidually.
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Brendan Hamm
2017-12-29
You can resize an image with the imresize function. Now since your images are of size 277x277x1 I will assume they are grayscale, but AlexNet was trained with RGB values and are thus 227x227x 3. It is not possible for you to recover color information from a grayscale image. You may be required to retrain the entire network with grayscale images.
I suggest for more questions on getting started with Deep Learning you check out the FREE Deep Learning OnRamp: https://matlabacademy.mathworks.com/
9 个评论
arun anoop m
2020-7-17
Walter Roberson
2023-1-22
These days you should use an augmented image datastore as that can automatically resize and automatically convert to gray or rgb.
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Ayshath Afra
2020-4-2
The training images are of size 256x256x3 but the input layer expects images of size 224x224x3.
pls help me to solve this error
1 个评论
Michael Keeling
2020-6-5
编辑:Michael Keeling
2020-6-5
You can make an augmented datastore of the images that resizes them auomatically, while leaving your raw images the same
Zaheer Hussain Fida
2023-1-10
can we extract the binary images features using alexnet.
1 个评论
Walter Roberson
2023-1-12
Use https://www.mathworks.com/help/matlab/ref/matlab.io.datastore.transform.html to apply double() to the binary images so that they become grayscale images containing 0 (false) and 1 (true).
Use augmentedImageDatastore to automatically apply any rescaling needed to get the size needed by alexnet, 227 x 227 x 3 -- there is a color conversion option that can be used in the function.
And then hope that alexnet just happens to have been trained on a number of binary images with useful class names, so that instead of classifying as (say) 'Mathworks Screwdriver', it classifies as (say) 'Solidity 0.8 Eccentricity 0.14 MajorAxes 85' .
... But I must admit, I think the chances are low.
I think you would have more success if you used Transfer Learning rather than hoping that alexnet is already able to identify the exact set of binary features that you want.
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