readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - f1
  model-index:
  - name: vit-base-riego
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: imagefolder
        type: imagefolder
        config: MaxP--agro_riego
        split: train
        args: MaxP--agro_riego
      metrics:
      - name: F1
        type: f1
        value: 0.9853801169590642
  ---
  
  <!-- 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-base-riego
  
  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: 0.0544
  - F1: 0.9854
  
  ## 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: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.0443        | 0.48  | 100  | 0.4737          | 0.8742 |
  | 0.0349        | 0.95  | 200  | 0.0774          | 0.9733 |
  | 0.003         | 1.43  | 300  | 0.0675          | 0.9794 |
  | 0.002         | 1.9   | 400  | 0.0674          | 0.9810 |
  | 0.0014        | 2.38  | 500  | 0.0431          | 0.9871 |
  | 0.0011        | 2.86  | 600  | 0.0544          | 0.9854 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  