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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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- image-segmentation |
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- vision |
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- generated_from_trainer |
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model-index: |
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- name: new_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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# new_ecc_segformer |
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ECC_crackdataset_withsplit dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0663 |
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- Mean Iou: 0.1943 |
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- Mean Accuracy: 0.3915 |
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- Overall Accuracy: 0.3915 |
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- Accuracy Background: nan |
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- Accuracy Crack: 0.3915 |
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- Iou Background: 0.0 |
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- Iou Crack: 0.3887 |
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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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| 0.0489 | 1.0 | 438 | 0.0634 | 0.1464 | 0.2933 | 0.2933 | nan | 0.2933 | 0.0 | 0.2929 | |
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| 0.0542 | 2.0 | 876 | 0.0439 | 0.1956 | 0.3917 | 0.3917 | nan | 0.3917 | 0.0 | 0.3912 | |
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| 0.0484 | 3.0 | 1314 | 0.0434 | 0.1719 | 0.3551 | 0.3551 | nan | 0.3551 | 0.0 | 0.3439 | |
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| 0.0539 | 4.0 | 1752 | 0.0447 | 0.1871 | 0.3820 | 0.3820 | nan | 0.3820 | 0.0 | 0.3741 | |
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| 0.0565 | 5.0 | 2190 | 0.0435 | 0.1888 | 0.3937 | 0.3937 | nan | 0.3937 | 0.0 | 0.3777 | |
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| 0.0544 | 6.0 | 2628 | 0.0442 | 0.1904 | 0.3930 | 0.3930 | nan | 0.3930 | 0.0 | 0.3808 | |
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| 0.0421 | 7.0 | 3066 | 0.0449 | 0.2256 | 0.4651 | 0.4651 | nan | 0.4651 | 0.0 | 0.4513 | |
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| 0.0352 | 8.0 | 3504 | 0.0587 | 0.1569 | 0.3165 | 0.3165 | nan | 0.3165 | 0.0 | 0.3138 | |
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| 0.0394 | 9.0 | 3942 | 0.0442 | 0.1842 | 0.3710 | 0.3710 | nan | 0.3710 | 0.0 | 0.3684 | |
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| 0.0445 | 10.0 | 4380 | 0.0609 | 0.1167 | 0.4173 | 0.4173 | nan | 0.4173 | 0.0 | 0.2334 | |
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| 0.0503 | 11.0 | 4818 | 0.0504 | 0.1702 | 0.3714 | 0.3714 | nan | 0.3714 | 0.0 | 0.3403 | |
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| 0.0379 | 12.0 | 5256 | 0.0460 | 0.1903 | 0.3869 | 0.3869 | nan | 0.3869 | 0.0 | 0.3807 | |
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| 0.0405 | 13.0 | 5694 | 0.0452 | 0.2017 | 0.4084 | 0.4084 | nan | 0.4084 | 0.0 | 0.4034 | |
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| 0.0367 | 14.0 | 6132 | 0.0477 | 0.1995 | 0.4060 | 0.4060 | nan | 0.4060 | 0.0 | 0.3990 | |
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| 0.0315 | 15.0 | 6570 | 0.0498 | 0.2073 | 0.4208 | 0.4208 | nan | 0.4208 | 0.0 | 0.4147 | |
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| 0.0244 | 16.0 | 7008 | 0.0486 | 0.1963 | 0.4029 | 0.4029 | nan | 0.4029 | 0.0 | 0.3926 | |
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| 0.031 | 17.0 | 7446 | 0.0568 | 0.1927 | 0.3892 | 0.3892 | nan | 0.3892 | 0.0 | 0.3855 | |
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| 0.0288 | 18.0 | 7884 | 0.0560 | 0.2033 | 0.4092 | 0.4092 | nan | 0.4092 | 0.0 | 0.4067 | |
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| 0.0354 | 19.0 | 8322 | 0.0613 | 0.2007 | 0.4056 | 0.4056 | nan | 0.4056 | 0.0 | 0.4013 | |
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| 0.0315 | 20.0 | 8760 | 0.0605 | 0.1865 | 0.3752 | 0.3752 | nan | 0.3752 | 0.0 | 0.3731 | |
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| 0.0343 | 21.0 | 9198 | 0.0653 | 0.1991 | 0.4019 | 0.4019 | nan | 0.4019 | 0.0 | 0.3981 | |
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| 0.0327 | 22.0 | 9636 | 0.0660 | 0.1945 | 0.3924 | 0.3924 | nan | 0.3924 | 0.0 | 0.3891 | |
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| 0.0252 | 22.83 | 10000 | 0.0663 | 0.1943 | 0.3915 | 0.3915 | nan | 0.3915 | 0.0 | 0.3887 | |
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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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