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---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-wrinkle
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-wrinkle
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the face-wrinkles dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0189
- Mean Iou: 0.2163
- Mean Accuracy: 0.4327
- Overall Accuracy: 0.4327
- Accuracy Unlabeled: nan
- Accuracy Wrinkle: 0.4327
- Iou Unlabeled: 0.0
- Iou Wrinkle: 0.4327
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Wrinkle | Iou Unlabeled | Iou Wrinkle |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:-------------:|:-----------:|
| 0.0122 | 0.1786 | 20 | 0.0186 | 0.1899 | 0.3798 | 0.3798 | nan | 0.3798 | 0.0 | 0.3798 |
| 0.0114 | 0.3571 | 40 | 0.0188 | 0.2007 | 0.4014 | 0.4014 | nan | 0.4014 | 0.0 | 0.4014 |
| 0.0104 | 0.5357 | 60 | 0.0189 | 0.2127 | 0.4254 | 0.4254 | nan | 0.4254 | 0.0 | 0.4254 |
| 0.0116 | 0.7143 | 80 | 0.0187 | 0.2215 | 0.4430 | 0.4430 | nan | 0.4430 | 0.0 | 0.4430 |
| 0.0104 | 0.8929 | 100 | 0.0189 | 0.1815 | 0.3630 | 0.3630 | nan | 0.3630 | 0.0 | 0.3630 |
| 0.0151 | 1.0714 | 120 | 0.0187 | 0.1949 | 0.3898 | 0.3898 | nan | 0.3898 | 0.0 | 0.3898 |
| 0.0155 | 1.25 | 140 | 0.0187 | 0.2073 | 0.4147 | 0.4147 | nan | 0.4147 | 0.0 | 0.4147 |
| 0.0077 | 1.4286 | 160 | 0.0192 | 0.2406 | 0.4812 | 0.4812 | nan | 0.4812 | 0.0 | 0.4812 |
| 0.0117 | 1.6071 | 180 | 0.0191 | 0.2391 | 0.4782 | 0.4782 | nan | 0.4782 | 0.0 | 0.4782 |
| 0.0063 | 1.7857 | 200 | 0.0188 | 0.1787 | 0.3573 | 0.3573 | nan | 0.3573 | 0.0 | 0.3573 |
| 0.01 | 1.9643 | 220 | 0.0185 | 0.2195 | 0.4389 | 0.4389 | nan | 0.4389 | 0.0 | 0.4389 |
| 0.0109 | 2.1429 | 240 | 0.0191 | 0.1699 | 0.3398 | 0.3398 | nan | 0.3398 | 0.0 | 0.3398 |
| 0.0104 | 2.3214 | 260 | 0.0191 | 0.2167 | 0.4335 | 0.4335 | nan | 0.4335 | 0.0 | 0.4335 |
| 0.0145 | 2.5 | 280 | 0.0198 | 0.2604 | 0.5208 | 0.5208 | nan | 0.5208 | 0.0 | 0.5208 |
| 0.0093 | 2.6786 | 300 | 0.0185 | 0.1963 | 0.3927 | 0.3927 | nan | 0.3927 | 0.0 | 0.3927 |
| 0.0106 | 2.8571 | 320 | 0.0185 | 0.2080 | 0.4159 | 0.4159 | nan | 0.4159 | 0.0 | 0.4159 |
| 0.007 | 3.0357 | 340 | 0.0190 | 0.1894 | 0.3787 | 0.3787 | nan | 0.3787 | 0.0 | 0.3787 |
| 0.01 | 3.2143 | 360 | 0.0189 | 0.2194 | 0.4389 | 0.4389 | nan | 0.4389 | 0.0 | 0.4389 |
| 0.0118 | 3.3929 | 380 | 0.0186 | 0.2312 | 0.4625 | 0.4625 | nan | 0.4625 | 0.0 | 0.4625 |
| 0.008 | 3.5714 | 400 | 0.0189 | 0.1746 | 0.3492 | 0.3492 | nan | 0.3492 | 0.0 | 0.3492 |
| 0.0101 | 3.75 | 420 | 0.0185 | 0.1822 | 0.3644 | 0.3644 | nan | 0.3644 | 0.0 | 0.3644 |
| 0.0093 | 3.9286 | 440 | 0.0187 | 0.2126 | 0.4252 | 0.4252 | nan | 0.4252 | 0.0 | 0.4252 |
| 0.008 | 4.1071 | 460 | 0.0186 | 0.2058 | 0.4116 | 0.4116 | nan | 0.4116 | 0.0 | 0.4116 |
| 0.0134 | 4.2857 | 480 | 0.0187 | 0.2335 | 0.4669 | 0.4669 | nan | 0.4669 | 0.0 | 0.4669 |
| 0.0119 | 4.4643 | 500 | 0.0191 | 0.1850 | 0.3700 | 0.3700 | nan | 0.3700 | 0.0 | 0.3700 |
| 0.0064 | 4.6429 | 520 | 0.0187 | 0.1892 | 0.3785 | 0.3785 | nan | 0.3785 | 0.0 | 0.3785 |
| 0.0087 | 4.8214 | 540 | 0.0190 | 0.2253 | 0.4506 | 0.4506 | nan | 0.4506 | 0.0 | 0.4506 |
| 0.0122 | 5.0 | 560 | 0.0196 | 0.2598 | 0.5196 | 0.5196 | nan | 0.5196 | 0.0 | 0.5196 |
| 0.0071 | 5.1786 | 580 | 0.0188 | 0.2224 | 0.4448 | 0.4448 | nan | 0.4448 | 0.0 | 0.4448 |
| 0.0125 | 5.3571 | 600 | 0.0188 | 0.2051 | 0.4103 | 0.4103 | nan | 0.4103 | 0.0 | 0.4103 |
| 0.0093 | 5.5357 | 620 | 0.0192 | 0.2410 | 0.4821 | 0.4821 | nan | 0.4821 | 0.0 | 0.4821 |
| 0.0082 | 5.7143 | 640 | 0.0191 | 0.2291 | 0.4582 | 0.4582 | nan | 0.4582 | 0.0 | 0.4582 |
| 0.0089 | 5.8929 | 660 | 0.0187 | 0.1993 | 0.3985 | 0.3985 | nan | 0.3985 | 0.0 | 0.3985 |
| 0.0104 | 6.0714 | 680 | 0.0191 | 0.2049 | 0.4098 | 0.4098 | nan | 0.4098 | 0.0 | 0.4098 |
| 0.0111 | 6.25 | 700 | 0.0187 | 0.2216 | 0.4431 | 0.4431 | nan | 0.4431 | 0.0 | 0.4431 |
| 0.0113 | 6.4286 | 720 | 0.0196 | 0.2525 | 0.5050 | 0.5050 | nan | 0.5050 | 0.0 | 0.5050 |
| 0.0099 | 6.6071 | 740 | 0.0189 | 0.2219 | 0.4439 | 0.4439 | nan | 0.4439 | 0.0 | 0.4439 |
| 0.0062 | 6.7857 | 760 | 0.0187 | 0.2349 | 0.4699 | 0.4699 | nan | 0.4699 | 0.0 | 0.4699 |
| 0.0132 | 6.9643 | 780 | 0.0188 | 0.2108 | 0.4217 | 0.4217 | nan | 0.4217 | 0.0 | 0.4217 |
| 0.0132 | 7.1429 | 800 | 0.0190 | 0.2097 | 0.4194 | 0.4194 | nan | 0.4194 | 0.0 | 0.4194 |
| 0.0141 | 7.3214 | 820 | 0.0187 | 0.2125 | 0.4251 | 0.4251 | nan | 0.4251 | 0.0 | 0.4251 |
| 0.0121 | 7.5 | 840 | 0.0189 | 0.2176 | 0.4351 | 0.4351 | nan | 0.4351 | 0.0 | 0.4351 |
| 0.0099 | 7.6786 | 860 | 0.0187 | 0.2002 | 0.4004 | 0.4004 | nan | 0.4004 | 0.0 | 0.4004 |
| 0.0168 | 7.8571 | 880 | 0.0188 | 0.2159 | 0.4319 | 0.4319 | nan | 0.4319 | 0.0 | 0.4319 |
| 0.0064 | 8.0357 | 900 | 0.0188 | 0.2194 | 0.4387 | 0.4387 | nan | 0.4387 | 0.0 | 0.4387 |
| 0.0121 | 8.2143 | 920 | 0.0191 | 0.2309 | 0.4618 | 0.4618 | nan | 0.4618 | 0.0 | 0.4618 |
| 0.0133 | 8.3929 | 940 | 0.0189 | 0.2101 | 0.4202 | 0.4202 | nan | 0.4202 | 0.0 | 0.4202 |
| 0.0105 | 8.5714 | 960 | 0.0190 | 0.2287 | 0.4573 | 0.4573 | nan | 0.4573 | 0.0 | 0.4573 |
| 0.0092 | 8.75 | 980 | 0.0188 | 0.2178 | 0.4356 | 0.4356 | nan | 0.4356 | 0.0 | 0.4356 |
| 0.0124 | 8.9286 | 1000 | 0.0191 | 0.2277 | 0.4553 | 0.4553 | nan | 0.4553 | 0.0 | 0.4553 |
| 0.0108 | 9.1071 | 1020 | 0.0189 | 0.2017 | 0.4033 | 0.4033 | nan | 0.4033 | 0.0 | 0.4033 |
| 0.0098 | 9.2857 | 1040 | 0.0190 | 0.2271 | 0.4542 | 0.4542 | nan | 0.4542 | 0.0 | 0.4542 |
| 0.0087 | 9.4643 | 1060 | 0.0189 | 0.2168 | 0.4335 | 0.4335 | nan | 0.4335 | 0.0 | 0.4335 |
| 0.008 | 9.6429 | 1080 | 0.0189 | 0.2219 | 0.4438 | 0.4438 | nan | 0.4438 | 0.0 | 0.4438 |
| 0.0071 | 9.8214 | 1100 | 0.0189 | 0.2204 | 0.4407 | 0.4407 | nan | 0.4407 | 0.0 | 0.4407 |
| 0.0072 | 10.0 | 1120 | 0.0189 | 0.2163 | 0.4327 | 0.4327 | nan | 0.4327 | 0.0 | 0.4327 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
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