---
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