readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: exper_batch_16_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_16_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: 0.3598
  - Accuracy: 0.9059
  
  ## 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: 4
  - mixed_precision_training: Apex, opt level O1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 3.7606        | 0.16  | 100  | 3.7839          | 0.1989   |
  | 3.1072        | 0.31  | 200  | 3.0251          | 0.3285   |
  | 2.4068        | 0.47  | 300  | 2.4380          | 0.4719   |
  | 2.0881        | 0.63  | 400  | 2.0489          | 0.5412   |
  | 1.6817        | 0.78  | 500  | 1.7968          | 0.6025   |
  | 1.342         | 0.94  | 600  | 1.5044          | 0.6249   |
  | 0.9343        | 1.1   | 700  | 1.1881          | 0.7132   |
  | 0.9552        | 1.25  | 800  | 1.1064          | 0.7224   |
  | 0.7265        | 1.41  | 900  | 0.9189          | 0.7768   |
  | 0.6732        | 1.56  | 1000 | 0.9227          | 0.7606   |
  | 0.5587        | 1.72  | 1100 | 0.7912          | 0.7903   |
  | 0.6332        | 1.88  | 1200 | 0.7606          | 0.7945   |
  | 0.3188        | 2.03  | 1300 | 0.6535          | 0.8288   |
  | 0.3079        | 2.19  | 1400 | 0.5686          | 0.8577   |
  | 0.2518        | 2.35  | 1500 | 0.5517          | 0.8577   |
  | 0.2           | 2.5   | 1600 | 0.5277          | 0.8631   |
  | 0.2032        | 2.66  | 1700 | 0.4841          | 0.8701   |
  | 0.1555        | 2.82  | 1800 | 0.4578          | 0.8793   |
  | 0.145         | 2.97  | 1900 | 0.4466          | 0.8755   |
  | 0.0985        | 3.13  | 2000 | 0.4249          | 0.8867   |
  | 0.0955        | 3.29  | 2100 | 0.3977          | 0.8932   |
  | 0.0438        | 3.44  | 2200 | 0.3785          | 0.9036   |
  | 0.0589        | 3.6   | 2300 | 0.3717          | 0.9017   |
  | 0.0709        | 3.76  | 2400 | 0.3609          | 0.9052   |
  | 0.0706        | 3.91  | 2500 | 0.3598          | 0.9059   |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.5.1
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  