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--- |
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license: apache-2.0 |
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base_model: microsoft/beit-base-finetuned-ade-640-640 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: BEiT_beit-base-finetuned-ade-640-640_Clean-Set3-Grayscale_RGB |
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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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# BEiT_beit-base-finetuned-ade-640-640_Clean-Set3-Grayscale_RGB |
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This model is a fine-tuned version of [microsoft/beit-base-finetuned-ade-640-640](https://huggingface.co/microsoft/beit-base-finetuned-ade-640-640) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0652 |
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- Mean Iou: 0.9446 |
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- Mean Accuracy: 0.9687 |
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- Overall Accuracy: 0.9855 |
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- Accuracy Background: 0.9893 |
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- Accuracy Melt: 0.9263 |
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- Accuracy Substrate: 0.9905 |
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- Iou Background: 0.9796 |
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- Iou Melt: 0.8757 |
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- Iou Substrate: 0.9785 |
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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: 2e-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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 200 |
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- num_epochs: 50 |
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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 Melt | Accuracy Substrate | Iou Background | Iou Melt | Iou Substrate | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:| |
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| 0.1893 | 3.7037 | 100 | 0.1358 | 0.9180 | 0.9486 | 0.9815 | 0.9923 | 0.8651 | 0.9884 | 0.9821 | 0.7997 | 0.9723 | |
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| 0.0951 | 7.4074 | 200 | 0.0746 | 0.9483 | 0.9791 | 0.9876 | 0.9909 | 0.9575 | 0.9890 | 0.9860 | 0.8775 | 0.9814 | |
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| 0.0892 | 11.1111 | 300 | 0.0631 | 0.9489 | 0.9772 | 0.9866 | 0.9875 | 0.9536 | 0.9903 | 0.9817 | 0.8854 | 0.9796 | |
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| 0.0878 | 14.8148 | 400 | 0.0692 | 0.9375 | 0.9687 | 0.9829 | 0.9853 | 0.9332 | 0.9877 | 0.9742 | 0.8632 | 0.9750 | |
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| 0.07 | 18.5185 | 500 | 0.0631 | 0.9419 | 0.9700 | 0.9844 | 0.9865 | 0.9339 | 0.9897 | 0.9777 | 0.8714 | 0.9767 | |
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| 0.0507 | 22.2222 | 600 | 0.0646 | 0.9379 | 0.9659 | 0.9829 | 0.9879 | 0.9230 | 0.9869 | 0.9738 | 0.8653 | 0.9747 | |
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| 0.0523 | 25.9259 | 700 | 0.0569 | 0.9485 | 0.9724 | 0.9864 | 0.9903 | 0.9371 | 0.9898 | 0.9809 | 0.8855 | 0.9791 | |
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| 0.0402 | 29.6296 | 800 | 0.0622 | 0.9422 | 0.9686 | 0.9848 | 0.9881 | 0.9279 | 0.9897 | 0.9777 | 0.8711 | 0.9778 | |
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| 0.0406 | 33.3333 | 900 | 0.0631 | 0.9427 | 0.9683 | 0.9852 | 0.9894 | 0.9256 | 0.9899 | 0.9790 | 0.8707 | 0.9785 | |
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| 0.0389 | 37.0370 | 1000 | 0.0650 | 0.9436 | 0.9695 | 0.9852 | 0.9879 | 0.9303 | 0.9904 | 0.9784 | 0.8739 | 0.9784 | |
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| 0.0396 | 40.7407 | 1100 | 0.0634 | 0.9457 | 0.9700 | 0.9857 | 0.9900 | 0.9301 | 0.9899 | 0.9798 | 0.8787 | 0.9787 | |
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| 0.036 | 44.4444 | 1200 | 0.0653 | 0.9443 | 0.9684 | 0.9855 | 0.9894 | 0.9254 | 0.9906 | 0.9797 | 0.8748 | 0.9786 | |
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| 0.0224 | 48.1481 | 1300 | 0.0652 | 0.9446 | 0.9687 | 0.9855 | 0.9893 | 0.9263 | 0.9905 | 0.9796 | 0.8757 | 0.9785 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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