readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-agrivision
    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: 0.9202733485193622
  ---
  
  <!-- 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-agrivision
  
  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: 0.3605
  - Accuracy: 0.9203
  
  ## 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: 30
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5913        | 1.0   | 31   | 0.7046          | 0.7175   |
  | 0.1409        | 2.0   | 62   | 0.8423          | 0.6788   |
  | 0.0825        | 3.0   | 93   | 0.6224          | 0.7654   |
  | 0.0509        | 4.0   | 124  | 0.4379          | 0.8360   |
  | 0.0439        | 5.0   | 155  | 0.1706          | 0.9317   |
  | 0.0107        | 6.0   | 186  | 0.1914          | 0.9362   |
  | 0.0134        | 7.0   | 217  | 0.2491          | 0.9089   |
  | 0.0338        | 8.0   | 248  | 0.2119          | 0.9362   |
  | 0.0306        | 9.0   | 279  | 0.4502          | 0.8610   |
  | 0.0054        | 10.0  | 310  | 0.4990          | 0.8747   |
  | 0.0033        | 11.0  | 341  | 0.2746          | 0.9112   |
  | 0.0021        | 12.0  | 372  | 0.2501          | 0.9317   |
  | 0.0068        | 13.0  | 403  | 0.1883          | 0.9522   |
  | 0.0038        | 14.0  | 434  | 0.3672          | 0.9134   |
  | 0.0006        | 15.0  | 465  | 0.2275          | 0.9408   |
  | 0.0011        | 16.0  | 496  | 0.3349          | 0.9134   |
  | 0.0017        | 17.0  | 527  | 0.3329          | 0.9157   |
  | 0.0007        | 18.0  | 558  | 0.2508          | 0.9317   |
  | 0.0023        | 19.0  | 589  | 0.2338          | 0.9385   |
  | 0.0003        | 20.0  | 620  | 0.3193          | 0.9226   |
  | 0.002         | 21.0  | 651  | 0.4604          | 0.9043   |
  | 0.0023        | 22.0  | 682  | 0.3338          | 0.9203   |
  | 0.005         | 23.0  | 713  | 0.2925          | 0.9271   |
  | 0.0001        | 24.0  | 744  | 0.2022          | 0.9522   |
  | 0.0002        | 25.0  | 775  | 0.2699          | 0.9339   |
  | 0.0007        | 26.0  | 806  | 0.2603          | 0.9385   |
  | 0.0005        | 27.0  | 837  | 0.4120          | 0.9134   |
  | 0.0003        | 28.0  | 868  | 0.3550          | 0.9203   |
  | 0.0008        | 29.0  | 899  | 0.3657          | 0.9203   |
  | 0.0           | 30.0  | 930  | 0.3605          | 0.9203   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  