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--- |
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license: other |
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base_model: ironchanchellor/segformer-b0_DsA |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: segformer-b0_DsApollo_ILT |
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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_DsApollo_ILT |
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This model is a fine-tuned version of [ironchanchellor/segformer-b0_DsA](https://huggingface.co/ironchanchellor/segformer-b0_DsA) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0116 |
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- Mean Iou: 0.7963 |
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- Mean Accuracy: 0.9731 |
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- Overall Accuracy: 0.9942 |
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- Accuracy Background: nan |
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- Accuracy Haz: 0.9965 |
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- Accuracy Matrix: 0.9883 |
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- Accuracy Porosity: 0.9077 |
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- Accuracy Carbides: 0.9757 |
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- Accuracy Substrate: 0.9970 |
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- Iou Background: 0.0 |
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- Iou Haz: 0.9932 |
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- Iou Matrix: 0.9767 |
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- Iou Porosity: 0.8590 |
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- Iou Carbides: 0.9547 |
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- Iou Substrate: 0.9942 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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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 Haz | Accuracy Matrix | Accuracy Porosity | Accuracy Carbides | Accuracy Substrate | Iou Background | Iou Haz | Iou Matrix | Iou Porosity | Iou Carbides | Iou Substrate | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------:|:---------------:|:-----------------:|:-----------------:|:------------------:|:--------------:|:-------:|:----------:|:------------:|:------------:|:-------------:| |
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| 0.2652 | 1.0 | 591 | 0.0181 | 0.7893 | 0.9731 | 0.9914 | nan | 0.9952 | 0.9836 | 0.9148 | 0.9794 | 0.9926 | 0.0 | 0.9872 | 0.9741 | 0.8340 | 0.9516 | 0.9886 | |
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| 0.0167 | 2.0 | 1182 | 0.0196 | 0.7899 | 0.9662 | 0.9920 | nan | 0.9919 | 0.9849 | 0.8780 | 0.9793 | 0.9967 | 0.0 | 0.9884 | 0.9746 | 0.8346 | 0.9522 | 0.9899 | |
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| 0.0272 | 3.0 | 1773 | 0.0152 | 0.7895 | 0.9652 | 0.9923 | nan | 0.9970 | 0.9906 | 0.8787 | 0.9672 | 0.9926 | 0.0 | 0.9890 | 0.9753 | 0.8311 | 0.9512 | 0.9903 | |
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| 0.0117 | 4.0 | 2364 | 0.0131 | 0.7924 | 0.9663 | 0.9933 | nan | 0.9951 | 0.9902 | 0.8801 | 0.9698 | 0.9966 | 0.0 | 0.9912 | 0.9759 | 0.8425 | 0.9524 | 0.9924 | |
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| 0.0176 | 5.0 | 2955 | 0.0160 | 0.7919 | 0.9658 | 0.9930 | nan | 0.9939 | 0.9871 | 0.8731 | 0.9780 | 0.9968 | 0.0 | 0.9903 | 0.9760 | 0.8396 | 0.9536 | 0.9916 | |
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| 0.0772 | 6.0 | 3546 | 0.0125 | 0.7928 | 0.9671 | 0.9937 | nan | 0.9949 | 0.9858 | 0.8767 | 0.9804 | 0.9976 | 0.0 | 0.9922 | 0.9760 | 0.8419 | 0.9535 | 0.9933 | |
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| 0.0568 | 7.0 | 4137 | 0.0126 | 0.7950 | 0.9697 | 0.9937 | nan | 0.9978 | 0.9895 | 0.8933 | 0.9735 | 0.9946 | 0.0 | 0.9920 | 0.9766 | 0.8540 | 0.9540 | 0.9931 | |
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| 0.0228 | 8.0 | 4728 | 0.0158 | 0.7956 | 0.9766 | 0.9934 | nan | 0.9933 | 0.9882 | 0.9283 | 0.9749 | 0.9980 | 0.0 | 0.9911 | 0.9766 | 0.8591 | 0.9543 | 0.9923 | |
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| 0.0058 | 9.0 | 5319 | 0.0120 | 0.7960 | 0.9731 | 0.9940 | nan | 0.9958 | 0.9873 | 0.9068 | 0.9782 | 0.9972 | 0.0 | 0.9927 | 0.9766 | 0.8580 | 0.9547 | 0.9938 | |
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| 0.0038 | 10.0 | 5910 | 0.0116 | 0.7963 | 0.9731 | 0.9942 | nan | 0.9965 | 0.9883 | 0.9077 | 0.9757 | 0.9970 | 0.0 | 0.9932 | 0.9767 | 0.8590 | 0.9547 | 0.9942 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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