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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 [ydmeira/beit-finetuned-pokemon](https://huggingface.co/ydmeira/beit-finetuned-pokemon) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0222
- Mean Iou: 0.4964
- Mean Accuracy: 0.9927
- Overall Accuracy: 0.9927
- Per Category Iou: [0.0, 0.9927382211696605]
- Per Category Accuracy: [nan, 0.9927382211696605]
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------:|:-------------------------:|
| 0.044 | 0.11 | 500 | 0.0430 | 0.4929 | 0.9857 | 0.9857 | [0.0, 0.9857017551704262] | [nan, 0.9857017551704262] |
| 0.0495 | 0.21 | 1000 | 0.0345 | 0.4960 | 0.9920 | 0.9920 | [0.0, 0.9920118130744071] | [nan, 0.9920118130744071] |
| 0.0382 | 0.32 | 1500 | 0.0399 | 0.4947 | 0.9894 | 0.9894 | [0.0, 0.9893992290428889] | [nan, 0.9893992290428889] |
| 0.0361 | 0.43 | 2000 | 0.0311 | 0.4963 | 0.9926 | 0.9926 | [0.0, 0.9925511589842341] | [nan, 0.9925511589842341] |
| 0.04 | 0.53 | 2500 | 0.0722 | 0.4920 | 0.9840 | 0.9840 | [0.0, 0.9839730680037156] | [nan, 0.9839730680037156] |
| 0.0308 | 0.64 | 3000 | 0.0319 | 0.4977 | 0.9954 | 0.9954 | [0.0, 0.9954462252146663] | [nan, 0.9954462252146663] |
| 0.0391 | 0.75 | 3500 | 0.1028 | 0.4837 | 0.9674 | 0.9674 | [0.0, 0.9673708120597321] | [nan, 0.9673708120597321] |
| 0.0425 | 0.85 | 4000 | 0.0330 | 0.4973 | 0.9946 | 0.9946 | [0.0, 0.9946091381677958] | [nan, 0.9946091381677958] |
| 0.0321 | 0.96 | 4500 | 0.0259 | 0.4963 | 0.9925 | 0.9925 | [0.0, 0.9925195785900393] | [nan, 0.9925195785900393] |
| 0.031 | 1.07 | 5000 | 0.0270 | 0.4965 | 0.9930 | 0.9930 | [0.0, 0.9930111407071547] | [nan, 0.9930111407071547] |
| 0.0281 | 1.17 | 5500 | 0.0367 | 0.4933 | 0.9866 | 0.9866 | [0.0, 0.9865881607581373] | [nan, 0.9865881607581373] |
| 0.0325 | 1.28 | 6000 | 0.0327 | 0.4940 | 0.9880 | 0.9880 | [0.0, 0.9879893562856097] | [nan, 0.9879893562856097] |
| 0.0253 | 1.39 | 6500 | 0.0237 | 0.4968 | 0.9937 | 0.9937 | [0.0, 0.9936538460005984] | [nan, 0.9936538460005984] |
| 0.0258 | 1.49 | 7000 | 0.0241 | 0.4964 | 0.9928 | 0.9928 | [0.0, 0.9927783017073394] | [nan, 0.9927783017073394] |
| 0.0266 | 1.6 | 7500 | 0.0234 | 0.4962 | 0.9924 | 0.9924 | [0.0, 0.9923954115635184] | [nan, 0.9923954115635184] |
| 0.0223 | 1.71 | 8000 | 0.0264 | 0.4964 | 0.9928 | 0.9928 | [0.0, 0.9928421413266322] | [nan, 0.9928421413266322] |
| 0.0212 | 1.81 | 8500 | 0.0235 | 0.4960 | 0.9920 | 0.9920 | [0.0, 0.9920402354291824] | [nan, 0.9920402354291824] |
| 0.0196 | 1.92 | 9000 | 0.0222 | 0.4964 | 0.9927 | 0.9927 | [0.0, 0.9927382211696605] | [nan, 0.9927382211696605] |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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