readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: exper_batch_32_e4
    results: []
  ---
  
  <!-- 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. -->
  
  # exper_batch_32_e4
  
  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 sudo-s/herbier_mesuem1 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3909
  - Accuracy: 0.9067
  
  ## 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: 32
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  - mixed_precision_training: Apex, opt level O1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 3.4295        | 0.31  | 100  | 3.4027          | 0.2837   |
  | 2.5035        | 0.62  | 200  | 2.4339          | 0.5247   |
  | 1.6542        | 0.94  | 300  | 1.7690          | 0.6388   |
  | 1.1589        | 1.25  | 400  | 1.3106          | 0.7460   |
  | 0.9363        | 1.56  | 500  | 0.9977          | 0.7803   |
  | 0.6946        | 1.88  | 600  | 0.8138          | 0.8207   |
  | 0.3488        | 2.19  | 700  | 0.6593          | 0.8489   |
  | 0.2935        | 2.5   | 800  | 0.5725          | 0.8662   |
  | 0.2557        | 2.81  | 900  | 0.5088          | 0.8855   |
  | 0.1509        | 3.12  | 1000 | 0.4572          | 0.8971   |
  | 0.1367        | 3.44  | 1100 | 0.4129          | 0.9090   |
  | 0.1078        | 3.75  | 1200 | 0.3909          | 0.9067   |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.5.1
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  