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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: beit-finetuned-pokemon |
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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-finetuned-pokemon |
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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 the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0426 |
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- Mean Accuracy: 0.9851 |
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- Mean Iou: 0.4926 |
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- Overall Accuracy: 0.9851 |
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- Per Category Accuracy: [nan, 0.9851295328900131] |
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- Per Category Iou: [0.0, 0.9851295328900131] |
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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: 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: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy | Per Category Accuracy | Per Category Iou | |
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|:-------------:|:-----:|:----:|:---------------:|:-------------:|:--------:|:----------------:|:-------------------------:|:-------------------------:| |
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| 0.2845 | 0.05 | 250 | 0.1909 | 0.8750 | 0.4375 | 0.8750 | [nan, 0.8750296526422883] | [0.0, 0.8750296526422883] | |
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| 0.103 | 0.11 | 500 | 0.1987 | 0.9048 | 0.4524 | 0.9048 | [nan, 0.9047505435789185] | [0.0, 0.9047505435789185] | |
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| 0.091 | 0.16 | 750 | 0.2199 | 0.8935 | 0.4468 | 0.8935 | [nan, 0.8935388953867466] | [0.0, 0.8935388953867466] | |
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| 0.0787 | 0.21 | 1000 | 0.0498 | 0.9832 | 0.4916 | 0.9832 | [nan, 0.9832157481853218] | [0.0, 0.9832157481853218] | |
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| 0.0516 | 0.27 | 1250 | 0.0642 | 0.9767 | 0.4884 | 0.9767 | [nan, 0.9767367885585835] | [0.0, 0.9767367885585835] | |
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| 0.051 | 0.32 | 1500 | 0.0907 | 0.9582 | 0.4791 | 0.9582 | [nan, 0.9582013500039326] | [0.0, 0.9582013500039326] | |
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| 0.0518 | 0.37 | 1750 | 0.0813 | 0.9578 | 0.4789 | 0.9578 | [nan, 0.9577983594953152] | [0.0, 0.9577983594953152] | |
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| 0.038 | 0.43 | 2000 | 0.0394 | 0.9875 | 0.4937 | 0.9875 | [nan, 0.9874955917462267] | [0.0, 0.9874955917462267] | |
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| 0.0466 | 0.48 | 2250 | 0.0482 | 0.9831 | 0.4915 | 0.9831 | [nan, 0.9830982793221819] | [0.0, 0.9830982793221819] | |
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| 0.054 | 0.53 | 2500 | 0.0568 | 0.9818 | 0.4909 | 0.9818 | [nan, 0.9818346010498621] | [0.0, 0.9818346010498621] | |
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| 0.0356 | 0.59 | 2750 | 0.0330 | 0.9921 | 0.4961 | 0.9921 | [nan, 0.9921038026421615] | [0.0, 0.9921038026421615] | |
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| 0.0292 | 0.64 | 3000 | 0.0364 | 0.9893 | 0.4947 | 0.9893 | [nan, 0.9893293618878236] | [0.0, 0.9893293618878236] | |
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| 0.0252 | 0.69 | 3250 | 0.0607 | 0.9824 | 0.4912 | 0.9824 | [nan, 0.9823825882221607] | [0.0, 0.9823825882221607] | |
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| 0.0286 | 0.75 | 3500 | 0.0526 | 0.9830 | 0.4915 | 0.9830 | [nan, 0.9830357074898451] | [0.0, 0.9830357074898451] | |
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| 0.0297 | 0.8 | 3750 | 0.0403 | 0.9844 | 0.4922 | 0.9844 | [nan, 0.9843719475221174] | [0.0, 0.9843719475221174] | |
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| 0.0257 | 0.85 | 4000 | 0.0478 | 0.9848 | 0.4924 | 0.9848 | [nan, 0.9847944421751276] | [0.0, 0.9847944421751276] | |
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| 0.0271 | 0.91 | 4250 | 0.0340 | 0.9869 | 0.4935 | 0.9869 | [nan, 0.9869270221516337] | [0.0, 0.9869270221516337] | |
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| 0.0235 | 0.96 | 4500 | 0.0426 | 0.9851 | 0.4926 | 0.9851 | [nan, 0.9851295328900131] | [0.0, 0.9851295328900131] | |
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### Framework versions |
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- Transformers 4.21.2 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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