Comments (1)
ameraner
commented on August 26, 2024
Hi @maxpluspro, thanks for the issue!
Did you try to adapt the crop_size parameter at prediction time? See here
Line 52 in 59e81ae
It might be that you're still cropping the images as during the training step, hence the output is smaller... if you use the same size of your input image, it might work as you are expecting.
Regarding the comment that the features between the groud-truth and prediction don't match... I don't really agree, seems like the network is doing an ok job within the expected performance.... particularly considering the strange shapes present in the scene (I'm not even really sure what those yellow-ish areas are... erosion/deforestation?).
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Related Issues (13)
- Data split HOT 2
- Operands could not be broadcast together with shapes (13, 128, 13) (13, 128, 128) HOT 4
- Index Error HOT 6
- Training Parameters: Nadam optimizer, learning rate HOT 2
- Generating Single Number Testing Metric HOT 3
- Input img HOT 2
- Error message "ConcatOp : Dimensions of inputs should match" HOT 1
- About data split HOT 9
- dataIO
- Prediction model returns the inputed photo with clouds HOT 7
- NotImplementedError: Cannot convert a symbolic Tensor (lambda_17/strided_slice:0) to a numpy array HOT 1
- Deployment HOT 6
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