readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - dfl
  metrics:
  - f1
  model-index:
  - name: vit_for_dfl
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: dfl
        type: dfl
        config: small
        split: train
        args: small
      metrics:
      - name: F1
        type: f1
        value: 0.2452846975088968
  ---
  
  <!-- 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. -->
  
  # vit_for_dfl
  
  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 dfl dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1771
  - F1: 0.2453
  
  ## 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: 2
  - eval_batch_size: 2
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 8
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.0836        | 1.0   | 358  | 0.1841          | 0.2453 |
  | 0.207         | 2.0   | 716  | 0.1835          | 0.2453 |
  | 0.2325        | 3.0   | 1074 | 0.1771          | 0.2453 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  