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segformer-b0-finetuned-wrinkle

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  1. README.md +64 -54
  2. model.safetensors +1 -1
  3. runs/Dec15_10-38-30_390d36c6cf30/events.out.tfevents.1734259117.390d36c6cf30.209.0 +3 -0
  4. runs/Dec15_10-42-12_390d36c6cf30/events.out.tfevents.1734259337.390d36c6cf30.209.1 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -18,14 +18,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the face-wrinkles dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0188
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- - Mean Iou: 0.2008
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- - Mean Accuracy: 0.4015
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- - Overall Accuracy: 0.4015
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  - Accuracy Unlabeled: nan
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- - Accuracy Wrinkle: 0.4015
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  - Iou Unlabeled: 0.0
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- - Iou Wrinkle: 0.4015
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  ## Model description
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@@ -45,8 +45,8 @@ More information needed
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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: 8
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- - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
@@ -58,52 +58,62 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Wrinkle | Iou Unlabeled | Iou Wrinkle |
60
  |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|
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- | 0.0127 | 0.2174 | 20 | 0.0185 | 0.1901 | 0.3802 | 0.3802 | nan | 0.3802 | 0.0 | 0.3802 |
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- | 0.011 | 0.4348 | 40 | 0.0185 | 0.1886 | 0.3772 | 0.3772 | nan | 0.3772 | 0.0 | 0.3772 |
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- | 0.0109 | 0.6522 | 60 | 0.0190 | 0.1380 | 0.2761 | 0.2761 | nan | 0.2761 | 0.0 | 0.2761 |
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- | 0.0157 | 0.8696 | 80 | 0.0190 | 0.1587 | 0.3174 | 0.3174 | nan | 0.3174 | 0.0 | 0.3174 |
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- | 0.0155 | 1.0870 | 100 | 0.0187 | 0.2034 | 0.4068 | 0.4068 | nan | 0.4068 | 0.0 | 0.4068 |
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- | 0.0185 | 1.3043 | 120 | 0.0184 | 0.1819 | 0.3639 | 0.3639 | nan | 0.3639 | 0.0 | 0.3639 |
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- | 0.0164 | 1.5217 | 140 | 0.0192 | 0.2445 | 0.4890 | 0.4890 | nan | 0.4890 | 0.0 | 0.4890 |
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- | 0.0202 | 1.7391 | 160 | 0.0187 | 0.1624 | 0.3249 | 0.3249 | nan | 0.3249 | 0.0 | 0.3249 |
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- | 0.008 | 1.9565 | 180 | 0.0185 | 0.1828 | 0.3656 | 0.3656 | nan | 0.3656 | 0.0 | 0.3656 |
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- | 0.015 | 2.1739 | 200 | 0.0190 | 0.2415 | 0.4831 | 0.4831 | nan | 0.4831 | 0.0 | 0.4831 |
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- | 0.0119 | 2.3913 | 220 | 0.0186 | 0.2115 | 0.4230 | 0.4230 | nan | 0.4230 | 0.0 | 0.4230 |
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- | 0.0113 | 2.6087 | 240 | 0.0185 | 0.1545 | 0.3090 | 0.3090 | nan | 0.3090 | 0.0 | 0.3090 |
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- | 0.0125 | 2.8261 | 260 | 0.0187 | 0.1798 | 0.3597 | 0.3597 | nan | 0.3597 | 0.0 | 0.3597 |
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- | 0.0197 | 3.0435 | 280 | 0.0195 | 0.1493 | 0.2987 | 0.2987 | nan | 0.2987 | 0.0 | 0.2987 |
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- | 0.0116 | 3.2609 | 300 | 0.0190 | 0.1612 | 0.3224 | 0.3224 | nan | 0.3224 | 0.0 | 0.3224 |
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- | 0.013 | 3.4783 | 320 | 0.0184 | 0.2097 | 0.4193 | 0.4193 | nan | 0.4193 | 0.0 | 0.4193 |
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- | 0.0176 | 3.6957 | 340 | 0.0185 | 0.2268 | 0.4537 | 0.4537 | nan | 0.4537 | 0.0 | 0.4537 |
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- | 0.0122 | 3.9130 | 360 | 0.0185 | 0.2039 | 0.4079 | 0.4079 | nan | 0.4079 | 0.0 | 0.4079 |
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- | 0.014 | 4.1304 | 380 | 0.0185 | 0.1942 | 0.3885 | 0.3885 | nan | 0.3885 | 0.0 | 0.3885 |
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- | 0.0105 | 4.3478 | 400 | 0.0185 | 0.2202 | 0.4403 | 0.4403 | nan | 0.4403 | 0.0 | 0.4403 |
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- | 0.0113 | 4.5652 | 420 | 0.0187 | 0.1648 | 0.3296 | 0.3296 | nan | 0.3296 | 0.0 | 0.3296 |
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- | 0.0092 | 4.7826 | 440 | 0.0185 | 0.2044 | 0.4087 | 0.4087 | nan | 0.4087 | 0.0 | 0.4087 |
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- | 0.0175 | 5.0 | 460 | 0.0185 | 0.2160 | 0.4320 | 0.4320 | nan | 0.4320 | 0.0 | 0.4320 |
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- | 0.0124 | 5.2174 | 480 | 0.0190 | 0.2009 | 0.4018 | 0.4018 | nan | 0.4018 | 0.0 | 0.4018 |
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- | 0.0162 | 5.4348 | 500 | 0.0186 | 0.2431 | 0.4863 | 0.4863 | nan | 0.4863 | 0.0 | 0.4863 |
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- | 0.0203 | 5.6522 | 520 | 0.0185 | 0.2091 | 0.4181 | 0.4181 | nan | 0.4181 | 0.0 | 0.4181 |
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- | 0.0172 | 5.8696 | 540 | 0.0190 | 0.1700 | 0.3401 | 0.3401 | nan | 0.3401 | 0.0 | 0.3401 |
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- | 0.014 | 6.0870 | 560 | 0.0189 | 0.1771 | 0.3541 | 0.3541 | nan | 0.3541 | 0.0 | 0.3541 |
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- | 0.0191 | 6.3043 | 580 | 0.0189 | 0.1788 | 0.3575 | 0.3575 | nan | 0.3575 | 0.0 | 0.3575 |
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- | 0.0157 | 6.5217 | 600 | 0.0188 | 0.1986 | 0.3971 | 0.3971 | nan | 0.3971 | 0.0 | 0.3971 |
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- | 0.0128 | 6.7391 | 620 | 0.0187 | 0.2218 | 0.4436 | 0.4436 | nan | 0.4436 | 0.0 | 0.4436 |
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- | 0.0155 | 6.9565 | 640 | 0.0185 | 0.2099 | 0.4198 | 0.4198 | nan | 0.4198 | 0.0 | 0.4198 |
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- | 0.0132 | 7.1739 | 660 | 0.0189 | 0.1909 | 0.3819 | 0.3819 | nan | 0.3819 | 0.0 | 0.3819 |
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- | 0.0142 | 7.3913 | 680 | 0.0185 | 0.1892 | 0.3784 | 0.3784 | nan | 0.3784 | 0.0 | 0.3784 |
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- | 0.0113 | 7.6087 | 700 | 0.0187 | 0.1914 | 0.3828 | 0.3828 | nan | 0.3828 | 0.0 | 0.3828 |
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- | 0.0111 | 7.8261 | 720 | 0.0187 | 0.2136 | 0.4271 | 0.4271 | nan | 0.4271 | 0.0 | 0.4271 |
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- | 0.0094 | 8.0435 | 740 | 0.0188 | 0.1922 | 0.3843 | 0.3843 | nan | 0.3843 | 0.0 | 0.3843 |
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- | 0.0198 | 8.2609 | 760 | 0.0188 | 0.1911 | 0.3822 | 0.3822 | nan | 0.3822 | 0.0 | 0.3822 |
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- | 0.0164 | 8.4783 | 780 | 0.0189 | 0.1896 | 0.3792 | 0.3792 | nan | 0.3792 | 0.0 | 0.3792 |
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- | 0.0222 | 8.6957 | 800 | 0.0186 | 0.2178 | 0.4355 | 0.4355 | nan | 0.4355 | 0.0 | 0.4355 |
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- | 0.0108 | 8.9130 | 820 | 0.0190 | 0.1855 | 0.3710 | 0.3710 | nan | 0.3710 | 0.0 | 0.3710 |
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- | 0.0128 | 9.1304 | 840 | 0.0187 | 0.2006 | 0.4011 | 0.4011 | nan | 0.4011 | 0.0 | 0.4011 |
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- | 0.0143 | 9.3478 | 860 | 0.0187 | 0.2013 | 0.4026 | 0.4026 | nan | 0.4026 | 0.0 | 0.4026 |
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- | 0.0088 | 9.5652 | 880 | 0.0187 | 0.2020 | 0.4040 | 0.4040 | nan | 0.4040 | 0.0 | 0.4040 |
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- | 0.0127 | 9.7826 | 900 | 0.0188 | 0.2023 | 0.4046 | 0.4046 | nan | 0.4046 | 0.0 | 0.4046 |
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- | 0.0109 | 10.0 | 920 | 0.0188 | 0.2008 | 0.4015 | 0.4015 | nan | 0.4015 | 0.0 | 0.4015 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
18
 
19
  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the face-wrinkles dataset.
20
  It achieves the following results on the evaluation set:
21
+ - Loss: 0.0189
22
+ - Mean Iou: 0.2163
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+ - Mean Accuracy: 0.4327
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+ - Overall Accuracy: 0.4327
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  - Accuracy Unlabeled: nan
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+ - Accuracy Wrinkle: 0.4327
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  - Iou Unlabeled: 0.0
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+ - Iou Wrinkle: 0.4327
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  ## Model description
31
 
 
45
 
46
  The following hyperparameters were used during training:
47
  - learning_rate: 6e-05
48
+ - train_batch_size: 16
49
+ - eval_batch_size: 16
50
  - seed: 42
51
  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
 
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Wrinkle | Iou Unlabeled | Iou Wrinkle |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|
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+ | 0.0122 | 0.1786 | 20 | 0.0186 | 0.1899 | 0.3798 | 0.3798 | nan | 0.3798 | 0.0 | 0.3798 |
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+ | 0.0114 | 0.3571 | 40 | 0.0188 | 0.2007 | 0.4014 | 0.4014 | nan | 0.4014 | 0.0 | 0.4014 |
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+ | 0.0104 | 0.5357 | 60 | 0.0189 | 0.2127 | 0.4254 | 0.4254 | nan | 0.4254 | 0.0 | 0.4254 |
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+ | 0.0116 | 0.7143 | 80 | 0.0187 | 0.2215 | 0.4430 | 0.4430 | nan | 0.4430 | 0.0 | 0.4430 |
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+ | 0.0104 | 0.8929 | 100 | 0.0189 | 0.1815 | 0.3630 | 0.3630 | nan | 0.3630 | 0.0 | 0.3630 |
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+ | 0.0151 | 1.0714 | 120 | 0.0187 | 0.1949 | 0.3898 | 0.3898 | nan | 0.3898 | 0.0 | 0.3898 |
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+ | 0.0155 | 1.25 | 140 | 0.0187 | 0.2073 | 0.4147 | 0.4147 | nan | 0.4147 | 0.0 | 0.4147 |
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+ | 0.0077 | 1.4286 | 160 | 0.0192 | 0.2406 | 0.4812 | 0.4812 | nan | 0.4812 | 0.0 | 0.4812 |
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+ | 0.0117 | 1.6071 | 180 | 0.0191 | 0.2391 | 0.4782 | 0.4782 | nan | 0.4782 | 0.0 | 0.4782 |
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+ | 0.0063 | 1.7857 | 200 | 0.0188 | 0.1787 | 0.3573 | 0.3573 | nan | 0.3573 | 0.0 | 0.3573 |
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+ | 0.01 | 1.9643 | 220 | 0.0185 | 0.2195 | 0.4389 | 0.4389 | nan | 0.4389 | 0.0 | 0.4389 |
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+ | 0.0109 | 2.1429 | 240 | 0.0191 | 0.1699 | 0.3398 | 0.3398 | nan | 0.3398 | 0.0 | 0.3398 |
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+ | 0.0104 | 2.3214 | 260 | 0.0191 | 0.2167 | 0.4335 | 0.4335 | nan | 0.4335 | 0.0 | 0.4335 |
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+ | 0.0145 | 2.5 | 280 | 0.0198 | 0.2604 | 0.5208 | 0.5208 | nan | 0.5208 | 0.0 | 0.5208 |
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+ | 0.0093 | 2.6786 | 300 | 0.0185 | 0.1963 | 0.3927 | 0.3927 | nan | 0.3927 | 0.0 | 0.3927 |
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+ | 0.0106 | 2.8571 | 320 | 0.0185 | 0.2080 | 0.4159 | 0.4159 | nan | 0.4159 | 0.0 | 0.4159 |
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+ | 0.007 | 3.0357 | 340 | 0.0190 | 0.1894 | 0.3787 | 0.3787 | nan | 0.3787 | 0.0 | 0.3787 |
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+ | 0.01 | 3.2143 | 360 | 0.0189 | 0.2194 | 0.4389 | 0.4389 | nan | 0.4389 | 0.0 | 0.4389 |
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+ | 0.0118 | 3.3929 | 380 | 0.0186 | 0.2312 | 0.4625 | 0.4625 | nan | 0.4625 | 0.0 | 0.4625 |
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+ | 0.008 | 3.5714 | 400 | 0.0189 | 0.1746 | 0.3492 | 0.3492 | nan | 0.3492 | 0.0 | 0.3492 |
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+ | 0.0101 | 3.75 | 420 | 0.0185 | 0.1822 | 0.3644 | 0.3644 | nan | 0.3644 | 0.0 | 0.3644 |
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+ | 0.0093 | 3.9286 | 440 | 0.0187 | 0.2126 | 0.4252 | 0.4252 | nan | 0.4252 | 0.0 | 0.4252 |
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+ | 0.008 | 4.1071 | 460 | 0.0186 | 0.2058 | 0.4116 | 0.4116 | nan | 0.4116 | 0.0 | 0.4116 |
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+ | 0.0134 | 4.2857 | 480 | 0.0187 | 0.2335 | 0.4669 | 0.4669 | nan | 0.4669 | 0.0 | 0.4669 |
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+ | 0.0119 | 4.4643 | 500 | 0.0191 | 0.1850 | 0.3700 | 0.3700 | nan | 0.3700 | 0.0 | 0.3700 |
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+ | 0.0064 | 4.6429 | 520 | 0.0187 | 0.1892 | 0.3785 | 0.3785 | nan | 0.3785 | 0.0 | 0.3785 |
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+ | 0.0087 | 4.8214 | 540 | 0.0190 | 0.2253 | 0.4506 | 0.4506 | nan | 0.4506 | 0.0 | 0.4506 |
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+ | 0.0122 | 5.0 | 560 | 0.0196 | 0.2598 | 0.5196 | 0.5196 | nan | 0.5196 | 0.0 | 0.5196 |
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+ | 0.0071 | 5.1786 | 580 | 0.0188 | 0.2224 | 0.4448 | 0.4448 | nan | 0.4448 | 0.0 | 0.4448 |
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+ | 0.0125 | 5.3571 | 600 | 0.0188 | 0.2051 | 0.4103 | 0.4103 | nan | 0.4103 | 0.0 | 0.4103 |
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+ | 0.0093 | 5.5357 | 620 | 0.0192 | 0.2410 | 0.4821 | 0.4821 | nan | 0.4821 | 0.0 | 0.4821 |
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+ | 0.0082 | 5.7143 | 640 | 0.0191 | 0.2291 | 0.4582 | 0.4582 | nan | 0.4582 | 0.0 | 0.4582 |
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+ | 0.0089 | 5.8929 | 660 | 0.0187 | 0.1993 | 0.3985 | 0.3985 | nan | 0.3985 | 0.0 | 0.3985 |
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+ | 0.0104 | 6.0714 | 680 | 0.0191 | 0.2049 | 0.4098 | 0.4098 | nan | 0.4098 | 0.0 | 0.4098 |
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+ | 0.0111 | 6.25 | 700 | 0.0187 | 0.2216 | 0.4431 | 0.4431 | nan | 0.4431 | 0.0 | 0.4431 |
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+ | 0.0113 | 6.4286 | 720 | 0.0196 | 0.2525 | 0.5050 | 0.5050 | nan | 0.5050 | 0.0 | 0.5050 |
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+ | 0.0099 | 6.6071 | 740 | 0.0189 | 0.2219 | 0.4439 | 0.4439 | nan | 0.4439 | 0.0 | 0.4439 |
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+ | 0.0062 | 6.7857 | 760 | 0.0187 | 0.2349 | 0.4699 | 0.4699 | nan | 0.4699 | 0.0 | 0.4699 |
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+ | 0.0132 | 6.9643 | 780 | 0.0188 | 0.2108 | 0.4217 | 0.4217 | nan | 0.4217 | 0.0 | 0.4217 |
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+ | 0.0132 | 7.1429 | 800 | 0.0190 | 0.2097 | 0.4194 | 0.4194 | nan | 0.4194 | 0.0 | 0.4194 |
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+ | 0.0141 | 7.3214 | 820 | 0.0187 | 0.2125 | 0.4251 | 0.4251 | nan | 0.4251 | 0.0 | 0.4251 |
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+ | 0.0121 | 7.5 | 840 | 0.0189 | 0.2176 | 0.4351 | 0.4351 | nan | 0.4351 | 0.0 | 0.4351 |
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+ | 0.0099 | 7.6786 | 860 | 0.0187 | 0.2002 | 0.4004 | 0.4004 | nan | 0.4004 | 0.0 | 0.4004 |
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+ | 0.0168 | 7.8571 | 880 | 0.0188 | 0.2159 | 0.4319 | 0.4319 | nan | 0.4319 | 0.0 | 0.4319 |
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+ | 0.0064 | 8.0357 | 900 | 0.0188 | 0.2194 | 0.4387 | 0.4387 | nan | 0.4387 | 0.0 | 0.4387 |
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+ | 0.0121 | 8.2143 | 920 | 0.0191 | 0.2309 | 0.4618 | 0.4618 | nan | 0.4618 | 0.0 | 0.4618 |
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+ | 0.0133 | 8.3929 | 940 | 0.0189 | 0.2101 | 0.4202 | 0.4202 | nan | 0.4202 | 0.0 | 0.4202 |
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+ | 0.0105 | 8.5714 | 960 | 0.0190 | 0.2287 | 0.4573 | 0.4573 | nan | 0.4573 | 0.0 | 0.4573 |
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+ | 0.0092 | 8.75 | 980 | 0.0188 | 0.2178 | 0.4356 | 0.4356 | nan | 0.4356 | 0.0 | 0.4356 |
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+ | 0.0124 | 8.9286 | 1000 | 0.0191 | 0.2277 | 0.4553 | 0.4553 | nan | 0.4553 | 0.0 | 0.4553 |
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+ | 0.0108 | 9.1071 | 1020 | 0.0189 | 0.2017 | 0.4033 | 0.4033 | nan | 0.4033 | 0.0 | 0.4033 |
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+ | 0.0098 | 9.2857 | 1040 | 0.0190 | 0.2271 | 0.4542 | 0.4542 | nan | 0.4542 | 0.0 | 0.4542 |
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+ | 0.0087 | 9.4643 | 1060 | 0.0189 | 0.2168 | 0.4335 | 0.4335 | nan | 0.4335 | 0.0 | 0.4335 |
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+ | 0.008 | 9.6429 | 1080 | 0.0189 | 0.2219 | 0.4438 | 0.4438 | nan | 0.4438 | 0.0 | 0.4438 |
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+ | 0.0071 | 9.8214 | 1100 | 0.0189 | 0.2204 | 0.4407 | 0.4407 | nan | 0.4407 | 0.0 | 0.4407 |
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+ | 0.0072 | 10.0 | 1120 | 0.0189 | 0.2163 | 0.4327 | 0.4327 | nan | 0.4327 | 0.0 | 0.4327 |
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  ### Framework versions
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