readme: ---
  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: 0.0219
  - Mean Iou: 0.4955
  - Mean Accuracy: 0.9910
  - Overall Accuracy: 0.9910
  - Per Category Iou: [0.0, 0.9909617791470107]
  - Per Category Accuracy: [nan, 0.9909617791470107]
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou          | Per Category Accuracy     |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------:|:-------------------------:|
  | 0.0354        | 0.21  | 1000  | 0.0347          | 0.4978   | 0.9955        | 0.9955           | [0.0, 0.9955007125868244] | [nan, 0.9955007125868244] |
  | 0.0273        | 0.43  | 2000  | 0.0277          | 0.4951   | 0.9903        | 0.9903           | [0.0, 0.9902709092544748] | [nan, 0.9902709092544748] |
  | 0.0307        | 0.64  | 3000  | 0.0788          | 0.4875   | 0.9751        | 0.9751           | [0.0, 0.9750850921785902] | [nan, 0.9750850921785902] |
  | 0.0295        | 0.85  | 4000  | 0.0412          | 0.4939   | 0.9877        | 0.9877           | [0.0, 0.9877162657609527] | [nan, 0.9877162657609527] |
  | 0.0255        | 1.07  | 5000  | 0.0842          | 0.4862   | 0.9723        | 0.9723           | [0.0, 0.972304346385062]  | [nan, 0.972304346385062]  |
  | 0.0253        | 1.28  | 6000  | 0.0325          | 0.4950   | 0.9901        | 0.9901           | [0.0, 0.9900621363084688] | [nan, 0.9900621363084688] |
  | 0.0239        | 1.49  | 7000  | 0.0440          | 0.4917   | 0.9835        | 0.9835           | [0.0, 0.9834701005512881] | [nan, 0.9834701005512881] |
  | 0.0238        | 1.71  | 8000  | 0.0338          | 0.4950   | 0.9900        | 0.9900           | [0.0, 0.9899977115151821] | [nan, 0.9899977115151821] |
  | 0.0223        | 1.92  | 9000  | 0.0319          | 0.4950   | 0.9900        | 0.9900           | [0.0, 0.989994712810938]  | [nan, 0.989994712810938]  |
  | 0.0231        | 2.13  | 10000 | 0.0382          | 0.4921   | 0.9841        | 0.9841           | [0.0, 0.984106425591889]  | [nan, 0.984106425591889]  |
  | 0.0205        | 2.35  | 11000 | 0.0450          | 0.4926   | 0.9851        | 0.9851           | [0.0, 0.9851146530893756] | [nan, 0.9851146530893756] |
  | 0.0201        | 2.56  | 12000 | 0.0265          | 0.4954   | 0.9908        | 0.9908           | [0.0, 0.9908277212846449] | [nan, 0.9908277212846449] |
  | 0.0188        | 2.77  | 13000 | 0.0377          | 0.4933   | 0.9866        | 0.9866           | [0.0, 0.9865726862234793] | [nan, 0.9865726862234793] |
  | 0.0181        | 2.99  | 14000 | 0.0219          | 0.4955   | 0.9910        | 0.9910           | [0.0, 0.9909617791470107] | [nan, 0.9909617791470107] |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  