readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-woody_90epochs
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: imagefolder
        type: imagefolder
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8424242424242424
  ---
  
  <!-- 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. -->
  
  # swin-tiny-patch4-window7-224-finetuned-woody_90epochs
  
  This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4351
  - Accuracy: 0.8424
  
  ## 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: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 90
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6659        | 1.0   | 58   | 0.6216          | 0.6558   |
  | 0.6181        | 2.0   | 116  | 0.5616          | 0.7115   |
  | 0.5941        | 3.0   | 174  | 0.5464          | 0.7224   |
  | 0.5727        | 4.0   | 232  | 0.5368          | 0.7297   |
  | 0.573         | 5.0   | 290  | 0.4971          | 0.7539   |
  | 0.5724        | 6.0   | 348  | 0.4920          | 0.7467   |
  | 0.5584        | 7.0   | 406  | 0.4949          | 0.7564   |
  | 0.5352        | 8.0   | 464  | 0.5255          | 0.7406   |
  | 0.5857        | 9.0   | 522  | 0.4954          | 0.7515   |
  | 0.5352        | 10.0  | 580  | 0.4888          | 0.7455   |
  | 0.5161        | 11.0  | 638  | 0.5306          | 0.7224   |
  | 0.5457        | 12.0  | 696  | 0.4856          | 0.76     |
  | 0.5309        | 13.0  | 754  | 0.4647          | 0.7612   |
  | 0.5357        | 14.0  | 812  | 0.4688          | 0.7697   |
  | 0.5183        | 15.0  | 870  | 0.4830          | 0.7527   |
  | 0.4837        | 16.0  | 928  | 0.5238          | 0.7370   |
  | 0.51          | 17.0  | 986  | 0.4658          | 0.7745   |
  | 0.533         | 18.0  | 1044 | 0.4589          | 0.7673   |
  | 0.4808        | 19.0  | 1102 | 0.4375          | 0.7794   |
  | 0.4854        | 20.0  | 1160 | 0.4574          | 0.7745   |
  | 0.4708        | 21.0  | 1218 | 0.4738          | 0.7709   |
  | 0.4801        | 22.0  | 1276 | 0.4688          | 0.76     |
  | 0.4751        | 23.0  | 1334 | 0.4610          | 0.7648   |
  | 0.497         | 24.0  | 1392 | 0.5058          | 0.7624   |
  | 0.4767        | 25.0  | 1450 | 0.4709          | 0.7721   |
  | 0.4805        | 26.0  | 1508 | 0.4447          | 0.7697   |
  | 0.4557        | 27.0  | 1566 | 0.4558          | 0.7721   |
  | 0.4636        | 28.0  | 1624 | 0.4325          | 0.8036   |
  | 0.4285        | 29.0  | 1682 | 0.4526          | 0.7794   |
  | 0.4358        | 30.0  | 1740 | 0.4302          | 0.8048   |
  | 0.4257        | 31.0  | 1798 | 0.4373          | 0.7927   |
  | 0.4137        | 32.0  | 1856 | 0.4458          | 0.7903   |
  | 0.4389        | 33.0  | 1914 | 0.4522          | 0.7988   |
  | 0.4537        | 34.0  | 1972 | 0.4395          | 0.7927   |
  | 0.4249        | 35.0  | 2030 | 0.4348          | 0.8      |
  | 0.4244        | 36.0  | 2088 | 0.4650          | 0.7867   |
  | 0.4256        | 37.0  | 2146 | 0.4402          | 0.8012   |
  | 0.4118        | 38.0  | 2204 | 0.4394          | 0.7867   |
  | 0.4128        | 39.0  | 2262 | 0.4225          | 0.8133   |
  | 0.416         | 40.0  | 2320 | 0.4410          | 0.8073   |
  | 0.4211        | 41.0  | 2378 | 0.4464          | 0.8024   |
  | 0.3838        | 42.0  | 2436 | 0.4440          | 0.7976   |
  | 0.374         | 43.0  | 2494 | 0.4175          | 0.7903   |
  | 0.412         | 44.0  | 2552 | 0.4169          | 0.8109   |
  | 0.3746        | 45.0  | 2610 | 0.4243          | 0.8012   |
  | 0.3719        | 46.0  | 2668 | 0.4132          | 0.8242   |
  | 0.381         | 47.0  | 2726 | 0.4485          | 0.7988   |
  | 0.3708        | 48.0  | 2784 | 0.4200          | 0.8085   |
  | 0.3591        | 49.0  | 2842 | 0.4071          | 0.8279   |
  | 0.3762        | 50.0  | 2900 | 0.4428          | 0.8145   |
  | 0.3426        | 51.0  | 2958 | 0.4058          | 0.8158   |
  | 0.3541        | 52.0  | 3016 | 0.4470          | 0.8182   |
  | 0.3373        | 53.0  | 3074 | 0.4252          | 0.8194   |
  | 0.3303        | 54.0  | 3132 | 0