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segcrack9k_conglomerate_segformer

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update model card README.md

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@@ -15,14 +15,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0857
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- - Mean Iou: 0.0010
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- - Mean Accuracy: 0.0021
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- - Overall Accuracy: 0.0021
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  - Accuracy Background: nan
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- - Accuracy Crack: 0.0021
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  - Iou Background: 0.0
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- - Iou Crack: 0.0021
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  ## Model description
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@@ -41,7 +41,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 8e-05
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  - train_batch_size: 2
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  - eval_batch_size: 2
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  - seed: 42
@@ -53,55 +53,13 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
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- | 0.0716 | 0.02 | 100 | 0.1132 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
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- | 0.0708 | 0.04 | 200 | 0.1006 | 0.0001 | 0.0003 | 0.0003 | nan | 0.0003 | 0.0 | 0.0003 |
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- | 0.1661 | 0.06 | 300 | 0.1042 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
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- | 0.0601 | 0.08 | 400 | 0.1005 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
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- | 0.1034 | 0.1 | 500 | 0.0980 | 0.0237 | 0.0474 | 0.0474 | nan | 0.0474 | 0.0 | 0.0474 |
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- | 0.0581 | 0.12 | 600 | 0.0965 | 0.0003 | 0.0005 | 0.0005 | nan | 0.0005 | 0.0 | 0.0005 |
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- | 0.0561 | 0.14 | 700 | 0.1023 | 0.0038 | 0.0075 | 0.0075 | nan | 0.0075 | 0.0 | 0.0075 |
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- | 0.1034 | 0.16 | 800 | 0.0956 | 0.0002 | 0.0003 | 0.0003 | nan | 0.0003 | 0.0 | 0.0003 |
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- | 0.1341 | 0.18 | 900 | 0.0985 | 0.0185 | 0.0369 | 0.0369 | nan | 0.0369 | 0.0 | 0.0369 |
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- | 0.1988 | 0.2 | 1000 | 0.0946 | 0.0059 | 0.0118 | 0.0118 | nan | 0.0118 | 0.0 | 0.0118 |
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- | 0.0378 | 0.22 | 1100 | 0.0945 | 0.1402 | 0.2804 | 0.2804 | nan | 0.2804 | 0.0 | 0.2804 |
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- | 0.0582 | 0.24 | 1200 | 0.0907 | 0.0488 | 0.0976 | 0.0976 | nan | 0.0976 | 0.0 | 0.0976 |
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- | 0.1464 | 0.26 | 1300 | 0.0971 | 0.1701 | 0.3401 | 0.3401 | nan | 0.3401 | 0.0 | 0.3401 |
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- | 0.0601 | 0.28 | 1400 | 0.0893 | 0.0222 | 0.0444 | 0.0444 | nan | 0.0444 | 0.0 | 0.0444 |
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- | 0.0855 | 0.3 | 1500 | 0.0910 | 0.0307 | 0.0613 | 0.0613 | nan | 0.0613 | 0.0 | 0.0613 |
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- | 0.1167 | 0.32 | 1600 | 0.0895 | 0.0143 | 0.0286 | 0.0286 | nan | 0.0286 | 0.0 | 0.0286 |
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- | 0.0641 | 0.34 | 1700 | 0.0918 | 0.0073 | 0.0145 | 0.0145 | nan | 0.0145 | 0.0 | 0.0145 |
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- | 0.0621 | 0.36 | 1800 | 0.0927 | 0.0181 | 0.0363 | 0.0363 | nan | 0.0363 | 0.0 | 0.0363 |
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- | 0.0364 | 0.38 | 1900 | 0.0884 | 0.1397 | 0.2794 | 0.2794 | nan | 0.2794 | 0.0 | 0.2794 |
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- | 0.1394 | 0.4 | 2000 | 0.0903 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 |
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- | 0.0187 | 0.42 | 2100 | 0.0914 | 0.0124 | 0.0248 | 0.0248 | nan | 0.0248 | 0.0 | 0.0248 |
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- | 0.1842 | 0.44 | 2200 | 0.0908 | 0.0045 | 0.0090 | 0.0090 | nan | 0.0090 | 0.0 | 0.0090 |
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- | 0.0847 | 0.46 | 2300 | 0.0896 | 0.0031 | 0.0062 | 0.0062 | nan | 0.0062 | 0.0 | 0.0062 |
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- | 0.0556 | 0.48 | 2400 | 0.0871 | 0.0016 | 0.0033 | 0.0033 | nan | 0.0033 | 0.0 | 0.0033 |
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- | 0.0454 | 0.51 | 2500 | 0.0896 | 0.0005 | 0.0010 | 0.0010 | nan | 0.0010 | 0.0 | 0.0010 |
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- | 0.1411 | 0.53 | 2600 | 0.0876 | 0.0095 | 0.0190 | 0.0190 | nan | 0.0190 | 0.0 | 0.0190 |
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- | 0.1044 | 0.55 | 2700 | 0.0936 | 0.0001 | 0.0002 | 0.0002 | nan | 0.0002 | 0.0 | 0.0002 |
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- | 0.1299 | 0.57 | 2800 | 0.0938 | 0.0008 | 0.0017 | 0.0017 | nan | 0.0017 | 0.0 | 0.0017 |
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- | 0.0909 | 0.59 | 2900 | 0.0877 | 0.0012 | 0.0024 | 0.0024 | nan | 0.0024 | 0.0 | 0.0024 |
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- | 0.0981 | 0.61 | 3000 | 0.0914 | 0.0012 | 0.0024 | 0.0024 | nan | 0.0024 | 0.0 | 0.0024 |
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- | 0.0905 | 0.63 | 3100 | 0.0880 | 0.0077 | 0.0153 | 0.0153 | nan | 0.0153 | 0.0 | 0.0153 |
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- | 0.2111 | 0.65 | 3200 | 0.0877 | 0.0000 | 0.0001 | 0.0001 | nan | 0.0001 | 0.0 | 0.0001 |
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- | 0.3218 | 0.67 | 3300 | 0.0860 | 0.0036 | 0.0072 | 0.0072 | nan | 0.0072 | 0.0 | 0.0072 |
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- | 0.1134 | 0.69 | 3400 | 0.0864 | 0.0075 | 0.0151 | 0.0151 | nan | 0.0151 | 0.0 | 0.0151 |
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- | 0.2184 | 0.71 | 3500 | 0.0907 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 |
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- | 0.1779 | 0.73 | 3600 | 0.0877 | 0.0029 | 0.0059 | 0.0059 | nan | 0.0059 | 0.0 | 0.0059 |
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- | 0.3664 | 0.75 | 3700 | 0.0878 | 0.0001 | 0.0001 | 0.0001 | nan | 0.0001 | 0.0 | 0.0001 |
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- | 0.0365 | 0.77 | 3800 | 0.0870 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 |
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- | 0.0591 | 0.79 | 3900 | 0.0877 | 0.0000 | 0.0001 | 0.0001 | nan | 0.0001 | 0.0 | 0.0001 |
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- | 0.0719 | 0.81 | 4000 | 0.0871 | 0.0004 | 0.0008 | 0.0008 | nan | 0.0008 | 0.0 | 0.0008 |
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- | 0.0402 | 0.83 | 4100 | 0.0874 | 0.0011 | 0.0022 | 0.0022 | nan | 0.0022 | 0.0 | 0.0022 |
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- | 0.0814 | 0.85 | 4200 | 0.0887 | 0.0008 | 0.0017 | 0.0017 | nan | 0.0017 | 0.0 | 0.0017 |
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- | 0.0485 | 0.87 | 4300 | 0.0871 | 0.0025 | 0.0050 | 0.0050 | nan | 0.0050 | 0.0 | 0.0050 |
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- | 0.0487 | 0.89 | 4400 | 0.0864 | 0.0004 | 0.0007 | 0.0007 | nan | 0.0007 | 0.0 | 0.0007 |
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- | 0.0689 | 0.91 | 4500 | 0.0859 | 0.0002 | 0.0004 | 0.0004 | nan | 0.0004 | 0.0 | 0.0004 |
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- | 0.0782 | 0.93 | 4600 | 0.0858 | 0.0018 | 0.0036 | 0.0036 | nan | 0.0036 | 0.0 | 0.0036 |
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- | 0.2153 | 0.95 | 4700 | 0.0855 | 0.0004 | 0.0008 | 0.0008 | nan | 0.0008 | 0.0 | 0.0008 |
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- | 0.1974 | 0.97 | 4800 | 0.0860 | 0.0004 | 0.0009 | 0.0009 | nan | 0.0009 | 0.0 | 0.0009 |
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- | 0.0184 | 0.99 | 4900 | 0.0857 | 0.0010 | 0.0021 | 0.0021 | nan | 0.0021 | 0.0 | 0.0021 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0333
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+ - Mean Iou: 0.3608
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+ - Mean Accuracy: 0.7217
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+ - Overall Accuracy: 0.7217
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  - Accuracy Background: nan
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+ - Accuracy Crack: 0.7217
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  - Iou Background: 0.0
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+ - Iou Crack: 0.7217
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 6e-05
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  - train_batch_size: 2
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  - eval_batch_size: 2
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
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+ | 0.0259 | 0.14 | 1000 | 0.0404 | 0.3267 | 0.6534 | 0.6534 | nan | 0.6534 | 0.0 | 0.6534 |
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+ | 0.0186 | 0.27 | 2000 | 0.0378 | 0.3586 | 0.7172 | 0.7172 | nan | 0.7172 | 0.0 | 0.7172 |
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+ | 0.0348 | 0.41 | 3000 | 0.0375 | 0.3209 | 0.6418 | 0.6418 | nan | 0.6418 | 0.0 | 0.6418 |
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+ | 0.011 | 0.54 | 4000 | 0.0356 | 0.3496 | 0.6991 | 0.6991 | nan | 0.6991 | 0.0 | 0.6991 |
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+ | 0.0132 | 0.68 | 5000 | 0.0350 | 0.3459 | 0.6918 | 0.6918 | nan | 0.6918 | 0.0 | 0.6918 |
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+ | 0.0573 | 0.81 | 6000 | 0.0339 | 0.3575 | 0.7149 | 0.7149 | nan | 0.7149 | 0.0 | 0.7149 |
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+ | 0.1466 | 0.95 | 7000 | 0.0333 | 0.3608 | 0.7217 | 0.7217 | nan | 0.7217 | 0.0 | 0.7217 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions