Comments (4)
I believe there is a correction to be made:
pre,rec,f1,mae = f1_mae_torch(pred_val*255, gt, valid_dataset, i_test, mybins, hypar)
should be
pre,rec,f1,mae = f1_mae_torch(pred_val* 255, gt* 255, valid_dataset, i_test, mybins, hypar)
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I have the same issue. F1 is 0 always.
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Multiplying gt by 255 solved it for me
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I tried overfitting on fewer images [24] to see if issue is real, f1 error array and hence np.amax(f1) both are coming out to be 0. Any solutions?
The code has been updated, please try to use that to see whether the error still happens?
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Related Issues (20)
- unable to download DIS5K Dataset
- error HOT 1
- Logic behind F1-Score calculation
- DIS V2 HOT 1
- An error occurred when running Python train_valid_reference_main.py HOT 2
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- Need Suggestion for Result on High Resolution Images HOT 5
- Dataset for Segmenting Humans HOT 2
- How to train with multiple GPUs on one machine
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- Low quality model on High resolution Images
- -Closed-
- Bad performance on low contrast images
- Error to load the encoder weights
- Suggestion to train gt_encoder or not
- How many training sets are needed to train a specific task?
- WHY?💀💀💀
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