xinwucwp / faultSeg

Using synthetic datasets to train an end-to-end CNN for 3D fault segmentation (We are working on an improved version!)
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Problem of different predicted results #20

Open kasyful opened 6 months ago

kasyful commented 6 months ago

Dear Dr @llmpass and Dr @xinwucwp,

I would say this work is great contribution to fault interpretation study.

I try to recreate and build a similar model (same training dataset and same architecture), however when i inference the model to my datasets, it seems to detect bright amplitude, rather than fault itself.

image

I am genuinely appreciate if you could provide any ideas or suggestion why the model work this way.

Thanks.

llmpass commented 6 months ago

Could you normalize your image, say minus mean and divide by standard deviation before you performing the inference?

kasyful commented 6 months ago

Thank you for your prompt response @llmpass ,

I did tried both z-score and minmax scaler normalization, it showing almost similar results.

image

My apologies, DataGenerator function already performed z-score for the training datasets. I still couldnt understand why the model predict differently.

Ive go thru each Unet architecture and cross entrophy balanced but still couldnt figure it out.

Thank you for your help.

llmpass commented 6 months ago

Yes, that's strange. I'm sorry that I cannot help you too much this time.

suporange commented 5 months ago

Thank you for your prompt response @llmpass ,

I did tried both z-score and minmax scaler normalization, it showing almost similar results.

image

My apologies, DataGenerator function already performed z-score for the training datasets. I still couldnt understand why the model predict differently.

Ive go thru each Unet architecture and cross entrophy balanced but still couldnt figure it out.

Thank you for your help.

@kasyful Sir, i have met the same problem. Do you have any ideas?

kasyful commented 5 months ago

Thank you for your prompt response @llmpass , I did tried both z-score and minmax scaler normalization, it showing almost similar results. image My apologies, DataGenerator function already performed z-score for the training datasets. I still couldnt understand why the model predict differently. Ive go thru each Unet architecture and cross entrophy balanced but still couldnt figure it out. Thank you for your help.

@kasyful Sir, i have met the same problem. Do you have any ideas?

@suporange , you need to transpose the dataset before inference the model.