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---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: beit-finetuned-pokemon
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# beit-finetuned-pokemon
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.
It achieves the following results on the evaluation set:
- Loss: 0.0426
- Mean Accuracy: 0.9851
- Mean Iou: 0.4926
- Overall Accuracy: 0.9851
- Per Category Accuracy: [nan, 0.9851295328900131]
- Per Category Iou: [0.0, 0.9851295328900131]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy | Per Category Accuracy | Per Category Iou |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|:--------:|:----------------:|:-------------------------:|:-------------------------:|
| 0.2845 | 0.05 | 250 | 0.1909 | 0.8750 | 0.4375 | 0.8750 | [nan, 0.8750296526422883] | [0.0, 0.8750296526422883] |
| 0.103 | 0.11 | 500 | 0.1987 | 0.9048 | 0.4524 | 0.9048 | [nan, 0.9047505435789185] | [0.0, 0.9047505435789185] |
| 0.091 | 0.16 | 750 | 0.2199 | 0.8935 | 0.4468 | 0.8935 | [nan, 0.8935388953867466] | [0.0, 0.8935388953867466] |
| 0.0787 | 0.21 | 1000 | 0.0498 | 0.9832 | 0.4916 | 0.9832 | [nan, 0.9832157481853218] | [0.0, 0.9832157481853218] |
| 0.0516 | 0.27 | 1250 | 0.0642 | 0.9767 | 0.4884 | 0.9767 | [nan, 0.9767367885585835] | [0.0, 0.9767367885585835] |
| 0.051 | 0.32 | 1500 | 0.0907 | 0.9582 | 0.4791 | 0.9582 | [nan, 0.9582013500039326] | [0.0, 0.9582013500039326] |
| 0.0518 | 0.37 | 1750 | 0.0813 | 0.9578 | 0.4789 | 0.9578 | [nan, 0.9577983594953152] | [0.0, 0.9577983594953152] |
| 0.038 | 0.43 | 2000 | 0.0394 | 0.9875 | 0.4937 | 0.9875 | [nan, 0.9874955917462267] | [0.0, 0.9874955917462267] |
| 0.0466 | 0.48 | 2250 | 0.0482 | 0.9831 | 0.4915 | 0.9831 | [nan, 0.9830982793221819] | [0.0, 0.9830982793221819] |
| 0.054 | 0.53 | 2500 | 0.0568 | 0.9818 | 0.4909 | 0.9818 | [nan, 0.9818346010498621] | [0.0, 0.9818346010498621] |
| 0.0356 | 0.59 | 2750 | 0.0330 | 0.9921 | 0.4961 | 0.9921 | [nan, 0.9921038026421615] | [0.0, 0.9921038026421615] |
| 0.0292 | 0.64 | 3000 | 0.0364 | 0.9893 | 0.4947 | 0.9893 | [nan, 0.9893293618878236] | [0.0, 0.9893293618878236] |
| 0.0252 | 0.69 | 3250 | 0.0607 | 0.9824 | 0.4912 | 0.9824 | [nan, 0.9823825882221607] | [0.0, 0.9823825882221607] |
| 0.0286 | 0.75 | 3500 | 0.0526 | 0.9830 | 0.4915 | 0.9830 | [nan, 0.9830357074898451] | [0.0, 0.9830357074898451] |
| 0.0297 | 0.8 | 3750 | 0.0403 | 0.9844 | 0.4922 | 0.9844 | [nan, 0.9843719475221174] | [0.0, 0.9843719475221174] |
| 0.0257 | 0.85 | 4000 | 0.0478 | 0.9848 | 0.4924 | 0.9848 | [nan, 0.9847944421751276] | [0.0, 0.9847944421751276] |
| 0.0271 | 0.91 | 4250 | 0.0340 | 0.9869 | 0.4935 | 0.9869 | [nan, 0.9869270221516337] | [0.0, 0.9869270221516337] |
| 0.0235 | 0.96 | 4500 | 0.0426 | 0.9851 | 0.4926 | 0.9851 | [nan, 0.9851295328900131] | [0.0, 0.9851295328900131] |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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