readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: bags_results
    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: 1.0
  ---
  
  <!-- 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. -->
  
  # bags_results
  
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0002
  - Accuracy: 1.0
  
  ## 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: 0.0002
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.0485        | 0.23  | 100  | 0.0119          | 0.9974   |
  | 0.0029        | 0.45  | 200  | 0.0105          | 0.9983   |
  | 0.0029        | 0.68  | 300  | 0.0080          | 0.9974   |
  | 0.0331        | 0.91  | 400  | 0.0014          | 1.0      |
  | 0.0019        | 1.13  | 500  | 0.0105          | 0.9974   |
  | 0.0008        | 1.36  | 600  | 0.0058          | 0.9991   |
  | 0.0083        | 1.59  | 700  | 0.0133          | 0.9957   |
  | 0.0009        | 1.81  | 800  | 0.0028          | 0.9991   |
  | 0.0005        | 2.04  | 900  | 0.0030          | 0.9991   |
  | 0.0005        | 2.27  | 1000 | 0.0016          | 0.9991   |
  | 0.0024        | 2.49  | 1100 | 0.0004          | 1.0      |
  | 0.0003        | 2.72  | 1200 | 0.0004          | 1.0      |
  | 0.0003        | 2.95  | 1300 | 0.0016          | 0.9991   |
  | 0.0003        | 3.17  | 1400 | 0.0009          | 0.9991   |
  | 0.0003        | 3.4   | 1500 | 0.0003          | 1.0      |
  | 0.0002        | 3.63  | 1600 | 0.0002          | 1.0      |
  | 0.0002        | 3.85  | 1700 | 0.0002          | 1.0      |
  | 0.0002        | 4.08  | 1800 | 0.0002          | 1.0      |
  | 0.0002        | 4.31  | 1900 | 0.0002          | 1.0      |
  | 0.0002        | 4.54  | 2000 | 0.0002          | 1.0      |
  | 0.0002        | 4.76  | 2100 | 0.0002          | 1.0      |
  | 0.0002        | 4.99  | 2200 | 0.0002          | 1.0      |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  