Model save
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README.md
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---
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license: other
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base_model: nvidia/mit-b5
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tags:
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- generated_from_trainer
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model-index:
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- name: FINAL_ecc_segformer
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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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# FINAL_ecc_segformer
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0749
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- Mean Iou: 0.1968
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- Mean Accuracy: 0.3939
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- Overall Accuracy: 0.3939
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- Accuracy Background: nan
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- Accuracy Crack: 0.3939
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- Iou Background: 0.0
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- Iou Crack: 0.3936
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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: 2
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- eval_batch_size: 2
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- seed: 1337
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: polynomial
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- training_steps: 10000
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### Training results
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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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| 0.0534 | 1.0 | 548 | 0.0614 | 0.1368 | 0.2750 | 0.2750 | nan | 0.2750 | 0.0 | 0.2736 |
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| 0.058 | 2.0 | 1096 | 0.1018 | 0.2093 | 0.4238 | 0.4238 | nan | 0.4238 | 0.0 | 0.4186 |
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| 0.0482 | 3.0 | 1644 | 0.0508 | 0.1791 | 0.4315 | 0.4315 | nan | 0.4315 | 0.0 | 0.3582 |
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| 0.0338 | 4.0 | 2192 | 0.0569 | 0.1849 | 0.3716 | 0.3716 | nan | 0.3716 | 0.0 | 0.3698 |
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| 0.0395 | 5.0 | 2740 | 0.0597 | 0.1745 | 0.3506 | 0.3506 | nan | 0.3506 | 0.0 | 0.3490 |
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| 0.0372 | 6.0 | 3288 | 0.0509 | 0.2298 | 0.4635 | 0.4635 | nan | 0.4635 | 0.0 | 0.4597 |
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| 0.0402 | 7.0 | 3836 | 0.0620 | 0.1751 | 0.3507 | 0.3507 | nan | 0.3507 | 0.0 | 0.3503 |
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| 0.038 | 8.0 | 4384 | 0.0681 | 0.1905 | 0.3815 | 0.3815 | nan | 0.3815 | 0.0 | 0.3810 |
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| 0.0393 | 9.0 | 4932 | 0.0685 | 0.2213 | 0.4433 | 0.4433 | nan | 0.4433 | 0.0 | 0.4425 |
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| 0.0376 | 10.0 | 5480 | 0.0590 | 0.1962 | 0.3929 | 0.3929 | nan | 0.3929 | 0.0 | 0.3924 |
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| 0.0381 | 11.0 | 6028 | 0.0626 | 0.1891 | 0.3801 | 0.3801 | nan | 0.3801 | 0.0 | 0.3783 |
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| 0.034 | 12.0 | 6576 | 0.0623 | 0.2061 | 0.4162 | 0.4162 | nan | 0.4162 | 0.0 | 0.4122 |
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| 0.0301 | 13.0 | 7124 | 0.0831 | 0.1832 | 0.3669 | 0.3669 | nan | 0.3669 | 0.0 | 0.3664 |
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| 0.034 | 14.0 | 7672 | 0.0636 | 0.2059 | 0.4119 | 0.4119 | nan | 0.4119 | 0.0 | 0.4118 |
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| 0.0303 | 15.0 | 8220 | 0.0705 | 0.1931 | 0.3864 | 0.3864 | nan | 0.3864 | 0.0 | 0.3862 |
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| 0.0338 | 16.0 | 8768 | 0.0685 | 0.2101 | 0.4206 | 0.4206 | nan | 0.4206 | 0.0 | 0.4202 |
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| 0.0229 | 17.0 | 9316 | 0.0706 | 0.2099 | 0.4204 | 0.4204 | nan | 0.4204 | 0.0 | 0.4197 |
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| 0.0337 | 18.0 | 9864 | 0.0742 | 0.1982 | 0.3968 | 0.3968 | nan | 0.3968 | 0.0 | 0.3965 |
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| 0.0257 | 18.25 | 10000 | 0.0749 | 0.1968 | 0.3939 | 0.3939 | nan | 0.3939 | 0.0 | 0.3936 |
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### Framework versions
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- Transformers 4.34.1
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- Pytorch 2.1.0+cpu
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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