readme: ---
  license: other
  tags:
  - vision
  - image-segmentation
  - generated_from_trainer
  model-index:
  - name: segformer-b5-finetuned-magic-cards-230117-3
    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. -->
  
  # segformer-b5-finetuned-magic-cards-230117-3
  
  This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the andrewljohnson/magic_cards dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0691
  - Mean Iou: 0.6585
  - Mean Accuracy: 0.9878
  - Overall Accuracy: 0.9912
  - Accuracy Unlabeled: nan
  - Accuracy Front: 0.9978
  - Accuracy Back: 0.9777
  - Iou Unlabeled: 0.0
  - Iou Front: 0.9978
  - Iou Back: 0.9777
  
  ## 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: 1
  - eval_batch_size: 1
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Front | Accuracy Back | Iou Unlabeled | Iou Front | Iou Back |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:-------------:|:---------:|:--------:|
  | 1.2232        | 0.37  | 20   | 0.4691          | 0.6041   | 0.9201        | 0.9218           | nan                | 0.9252         | 0.9150        | 0.0           | 0.9252    | 0.8870   |
  | 0.2718        | 0.74  | 40   | 0.1983          | 0.6509   | 0.9764        | 0.9785           | nan                | 0.9826         | 0.9702        | 0.0           | 0.9826    | 0.9702   |
  | 0.255         | 1.11  | 60   | 0.0939          | 0.6524   | 0.9785        | 0.9794           | nan                | 0.9812         | 0.9758        | 0.0           | 0.9812    | 0.9758   |
  | 0.1103        | 1.48  | 80   | 0.0682          | 0.6536   | 0.9804        | 0.9813           | nan                | 0.9830         | 0.9779        | 0.0           | 0.9830    | 0.9779   |
  | 0.1373        | 1.85  | 100  | 0.1260          | 0.6631   | 0.9946        | 0.9961           | nan                | 0.9989         | 0.9903        | 0.0           | 0.9989    | 0.9903   |
  | 0.0566        | 2.22  | 120  | 0.1558          | 0.6578   | 0.9868        | 0.9912           | nan                | 0.9999         | 0.9736        | 0.0           | 0.9999    | 0.9736   |
  | 0.1535        | 2.59  | 140  | 0.1330          | 0.6558   | 0.9838        | 0.9883           | nan                | 0.9973         | 0.9703        | 0.0           | 0.9973    | 0.9703   |
  | 0.0586        | 2.96  | 160  | 0.2317          | 0.6599   | 0.9899        | 0.9933           | nan                | 1.0000         | 0.9798        | 0.0           | 1.0000    | 0.9798   |
  | 0.0727        | 3.33  | 180  | 0.1018          | 0.6586   | 0.9880        | 0.9919           | nan                | 0.9995         | 0.9764        | 0.0           | 0.9995    | 0.9764   |
  | 0.3588        | 3.7   | 200  | 0.1151          | 0.6608   | 0.9912        | 0.9939           | nan                | 0.9993         | 0.9831        | 0.0           | 0.9993    | 0.9831   |
  | 0.0463        | 4.07  | 220  | 0.0538          | 0.6610   | 0.9915        | 0.9934           | nan                | 0.9969         | 0.9862        | 0.0           | 0.9969    | 0.9862   |
  | 0.046         | 4.44  | 240  | 0.1201          | 0.6581   | 0.9871        | 0.9912           | nan                | 0.9991         | 0.9751        | 0.0           | 0.9991    | 0.9751   |
  | 0.0468        | 4.81  | 260  | 0.0691          | 0.6585   | 0.9878        | 0.9912           | nan                | 0.9978         | 0.9777        | 0.0           | 0.9978    | 0.9777   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1
  - Datasets 2.8.0
  - Tokenizers 0.13.0.dev0
  