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--- |
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library_name: transformers |
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license: other |
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base_model: nvidia/mit-b0 |
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tags: |
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- vision |
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- image-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: segformer-b0-finetuned-wrinkle |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# segformer-b0-finetuned-wrinkle |
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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: 0.0189 |
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- 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 |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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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: 16 |
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- eval_batch_size: 16 |
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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 |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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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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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.20.3 |
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